Update Catalog Prompt Slots Router and Access Layer Exemptions

- Added documentation to the `catalog_prompt_slots.py` file to clarify its role as a global admin catalog requiring authentication and admin role, without tenant context.
- Updated the `check_access_layer_hints.py` script to include `catalog_prompt_slots.py` in the list of exempt routers, ensuring proper access control for admin functionalities.

.claude/docs/PROJECT_STATUS.md

@@ -15,7 +15,7 @@
 
 **Plattform-Rechtstexte (P-01, 0.8.95–0.8.96):** Admin-Editor mit **Abschnitts- und Vollvorschau** (Markdown); fortlaufende Abschnittsnummerierung in der Anzeige/PDF (Darstellung, nicht DB-persistent).
 
-**Parallel weiter relevant:** **Trainingsplan Phasen & Streams** (Migration **063**, Coach + Planung **0.8.137–0.8.140**; Handover **`docs/HANDOVER.md`** §3); **Trainingsrahmenprogramm** (036–037), **Progressionsgraph** (032–034) — siehe **`TRAINING_FRAMEWORK_SPEC.md`**. **Planungs-KI Progressionsgraph** (Roadmap-first, Auto-Optimierung, Katalog-Kontext **0.8.233**): Ist-Doku **`docs/architecture/PLANNING_PROGRESSION_GRAPH_KI.md`**, Handover **`docs/HANDOVER.md`** §2.8.
+**Parallel weiter relevant:** **Trainingsplan Phasen & Streams** (Migration **063**, Coach + Planung **0.8.137–0.8.140**; Handover **`docs/HANDOVER.md`** §3); **Trainingsrahmenprogramm** (036–037), **Progressionsgraph** (032–034) — siehe **`TRAINING_FRAMEWORK_SPEC.md`**. **Planungs-KI Progressionsgraph** (Roadmap-first, Auto-Optimierung, Katalog-Kontext **0.8.233**, **F15** Match-Dialog + getrennte Pfad-QS lokal): Ist-Doku **`docs/architecture/PLANNING_PROGRESSION_GRAPH_KI.md`**, Handover **`docs/HANDOVER.md`** §2.8.
 
 **Referenz:** [`library/FEATURES_DELIVERED_2026-Q2.md`](library/FEATURES_DELIVERED_2026-Q2.md) Abschnitt 12 · Medien-Norm: [`technical/MEDIA_ASSETS_AND_ARCHIVE_SPEC.md`](technical/MEDIA_ASSETS_AND_ARCHIVE_SPEC.md) (inkl. **Abschnitt 11 Inline-Medien**, umgesetzt) · **Fachlicher Nutzerüberblick:** [`../../docs/FACHLICHE_NUTZERFUNKTIONEN.md`](../../docs/FACHLICHE_NUTZERFUNKTIONEN.md)
 

.claude/docs/working/PROGRESSION_GRAPH_SLOT_EDITOR_SPEC.md

@@ -1,6 +1,6 @@
-# Progressionsgraph — Slot-Editor (Phase B)
+# Progressionsgraph — Slot-Editor (Phase B + F15)
 
-**Stand:** 2026-06-10 · **Status:** In Umsetzung
+**Stand:** 2026-05-22 · **Status:** Umgesetzt (F14 + F15 lokal nach 0.8.233)
 
 ## Ziel
 
@@ -35,35 +35,52 @@ Leere Slots in der Roadmap sind erlaubt; Kanten nur zwischen aufeinanderfolgende
   slots: Slot[],           // index = major_step_index
   pathSkillExpectations?,
   lastFindings?,           // path_qa-Snapshot
+  findingsStale?: boolean, // Bewertung veraltet (↔ Artefakt findings_stale)
   dirty: boolean,
 }
 ```
 
 **Hydration:** `planning_roadmap` + Kanten → Slots; `slot_contents[]` für Entwürfe; Primärkette aus `next_exercise`.
 
-**Speichern:** Batch-Delete bestehender Pfad-/Schwester-Kanten → `edges/sequence` (Primärkette) → einzelne `sibling`-Kanten → `PUT`/`sequence` mit Artefakt inkl. `slot_contents`, optional `last_findings`.
+**Speichern:** Batch-Delete bestehender Pfad-/Schwester-Kanten → `edges/sequence` (Primärkette) → einzelne `sibling`-Kanten → `PUT`/`sequence` mit Artefakt inkl. `slot_contents`, `last_findings`, **`findings_stale`**.
 
 ## Findings-Panel
 
-Nutzt `path_qa` (`overall_ok`, `quality_score`, `issues`, `recommendations`, `gap_fill_offers`, …).
+Nutzt `path_qa`:
+
+| Feld | Bedeutung |
+|------|-----------|
+| `quality_score` | Gesamt = **min(`roadmap_qa`, `assignment_qa`)** |
+| `roadmap_qa` | Stufen/Roadmap (LLM `topic_coverage`, …) |
+| `assignment_qa` | Slot-Befüllung (`empty_slot_count`, …) |
+| `overall_ok`, `issues`, `recommendations`, `gap_fill_offers`, … | wie bisher |
 
 **API:** `POST /api/planning/progression-path-suggest` mit `evaluate_only: true` und `evaluate_steps[]` — QA ohne Re-Match.
 
-Persistenz: `planning_roadmap.last_findings`.
+**Bewertung veraltet:** Jede Graph-Änderung setzt `findingsStale: true` → Banner im Panel. Nach „Graph bewerten“ → `false`. Persistenz: `planning_roadmap.findings_stale`.
+
+## Match-Flow („Übungen matchen“)
+
+1. **Schritt 1:** `evaluate_only` + volle Pfad-QS (wie „Graph bewerten“)
+2. **Schritt 2:** `unified_slot_review: true` → **`ProgressionOptimizeCompareModal`**
+3. Pro Slot: aktuell vs. beste Bibliothek vs. optional KI-Vorschlag
+4. **Vorauswahl:** Bibliothek nur wenn Stufen-Fit ≥ 50 % und besser als Baseline; sonst KI (bei leerem/schwachem Slot)
+5. **Übernahme:** nur gewählte Slots speichern — **keine** automatische Nach-Bewertung
 
 ## Artefakt-Erweiterung (`GraphPlanningRoadmapArtifact`)
 
-Zusätzlich optional:
+Optional:
 
 - `slot_contents[]` — `{ major_step_index, primary, siblings[] }`
 - `last_findings` — letzter `path_qa`-Snapshot
+- **`findings_stale`** — bool, Bewertung bezieht sich nicht mehr auf aktuellen Graph-Stand
 
 ## UI (konsolidiert)
 
 - **Eine Oberfläche:** `ExerciseProgressionGraphPanel` embeddet `ProgressionGraphEditor` (Slots + Findings)
 - Kein separater Slot-Editor, kein 4-Schritt-KI-Wizard, kein `ProgressionChainEditor` im Panel
 - Route `/progression-graphs/:id` → Redirect nach `/exercises` (Deep-Link wählt Graph)
-- **Phase C:** Übersicht mit Kacheln (Name, Start, Ziel)
+- **Slot-Keys:** stabil `slot-{index}` (nicht Lernziel-Text) — sonst Fokusverlust beim Tippen
 
 ## Ersetzt (Legacy, nicht mehr im Panel)
 
@@ -71,11 +88,14 @@ Zusätzlich optional:
 
 ## Implementierungsreihenfolge
 
-| ID | Inhalt |
-|----|--------|
-| B.0 | Draft + Laden/Speichern Slots ↔ Kanten |
-| B.1 | Slot-Karten, Bibliothek + Entwurf |
-| B.2 | Findings-Panel + `evaluate_only` |
-| B.3 | Entwürfe im Artefakt + „Übung anlegen“ |
-| B.4 | Route + Panel vereinfachen |
-| B.5 | `last_findings` + Phase-C-Vorbereitung |
+| ID | Inhalt | Status |
+|----|--------|--------|
+| B.0 | Draft + Laden/Speichern Slots ↔ Kanten | ✅ |
+| B.1 | Slot-Karten, Bibliothek + Entwurf | ✅ |
+| B.2 | Findings-Panel + `evaluate_only` | ✅ |
+| B.3 | Entwürfe im Artefakt + „Übung anlegen“ | ✅ |
+| B.4 | Route + Panel vereinfachen | ✅ |
+| B.5 | `last_findings` + Phase-C-Vorbereitung | ✅ |
+| F15 | Unified Slot-Review, getrennte QS, `findings_stale` | ✅ |
+
+**Ist-Doku:** `docs/architecture/PLANNING_PROGRESSION_GRAPH_KI.md` §8.1 · `docs/HANDOVER.md` §2.8 F15

backend/ai_prompt_planning_preview.py

@@ -0,0 +1,317 @@
+"""
+Admin-Vorschau: Platzhalter für Planungs-Prompts (Progressionsgraph, Pfad-QS, Suggest).
+
+Nutzt repräsentative Beispieldaten + echte Katalog-Auszüge aus der DB.
+"""
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, List, Mapping, Optional
+
+from pydantic import BaseModel, Field
+
+from planning_exercise_semantics import brief_to_summary_dict, build_semantic_brief
+from planning_intent_context import build_planning_intent_context
+from planning_prompt_variables import merge_planning_prompt_variables
+
+PLANNING_PROMPT_SLUGS = frozenset(
+    {
+        "planning_progression_start_target",
+        "planning_progression_goal_analysis",
+        "planning_progression_roadmap",
+        "planning_progression_stage_spec",
+        "planning_exercise_query_semantics",
+        "planning_exercise_path_qa",
+        "planning_exercise_search_intent",
+        "planning_exercise_search_rank",
+        "planning_exercise_expectation_profile",
+    }
+)
+
+
+class PlanningPromptPreviewInput(BaseModel):
+    goal_query: str = Field(
+        default="Mae Geri vom Grundschritt bis zur kontrollierten Kumite-Nähe",
+        max_length=2000,
+    )
+    user_notes: str = Field(default="Fokus Breitensport, ohne Wettkampfdruck.", max_length=2000)
+    max_steps: int = Field(default=5, ge=2, le=10)
+    search_query: Optional[str] = Field(default=None, max_length=2000)
+    planning_catalog_context: Optional[Dict[str, Any]] = Field(default=None)
+
+
+def is_planning_prompt_slug(slug: str) -> bool:
+    return (slug or "").strip().lower() in PLANNING_PROMPT_SLUGS
+
+
+def _compact_json(obj: Any) -> str:
+    return json.dumps(obj, ensure_ascii=False, separators=(",", ":"))
+
+
+def _sample_goal_analysis() -> Dict[str, Any]:
+    return {
+        "primary_topic": "Mae Geri",
+        "start_assumption": "Grundstellung und einfache Frontkick-Bewegung bekannt",
+        "target_state": "Kontrollierter Mae Geri in Kumite-Nähe mit Hüftöffnung",
+        "success_criteria": [
+            "Hüfte öffnet vor dem Kick",
+            "Ballen trifft Zielzone",
+            "Rückzug ohne Balanceverlust",
+        ],
+        "constraints": {
+            "partner_required": False,
+            "excluded_themes": ["reine Kraft ohne Technikbezug"],
+            "trainer_notes": "Breitensport, kein Wettkampf",
+        },
+    }
+
+
+def _sample_major_steps(max_steps: int) -> List[Dict[str, Any]]:
+    phases = ["einstieg", "grundlage", "vertiefung", "anwendung", "perfektion"]
+    titles = [
+        "Grundstellung und Mae Geri Einstieg",
+        "Hüftöffnung und Ballen-Fokus",
+        "Koordination und Rückzug",
+        "Anwendung in Partnerübung",
+        "Qualität unter leichtem Druck",
+    ]
+    out: List[Dict[str, Any]] = []
+    for i in range(max_steps):
+        out.append(
+            {
+                "index": i,
+                "phase": phases[min(i, len(phases) - 1)],
+                "title": titles[min(i, len(titles) - 1)],
+                "learning_goal": titles[min(i, len(titles) - 1)],
+            }
+        )
+    return out
+
+
+def _sample_path_steps() -> List[Dict[str, Any]]:
+    return [
+        {
+            "index": 1,
+            "exercise_id": 101,
+            "title": "Mae Geri — Stand und Hüftöffnung",
+            "goal": "Frontkick mit geöffneter Hüfte aus Grundstellung",
+            "is_bridge": False,
+            "is_ai_proposal": False,
+            "reasons": ["Stufen-Gate: Grundlagen"],
+        },
+        {
+            "index": 2,
+            "exercise_id": 102,
+            "title": "Mae Geri — Ballen und Rückzug",
+            "goal": "Präziser Ballentreffer mit kontrolliertem Rückzug",
+            "is_bridge": False,
+            "is_ai_proposal": False,
+            "reasons": ["Nachfolger im Graph"],
+        },
+    ]
+
+
+def _sample_planning_context() -> Dict[str, Any]:
+    return {
+        "scope": "progression_path",
+        "goal_query": "Mae Geri vom Grundschritt bis zur Kumite-Nähe",
+        "stage_index": 1,
+        "learning_goal": "Hüftöffnung und Ballen-Fokus",
+    }
+
+
+def _sample_target_profile() -> Dict[str, Any]:
+    return {
+        "primary_focus": "Kihon",
+        "training_type": "Breitensport",
+        "skill_expectations": ["Geri Waza", "Koordination"],
+    }
+
+
+def _sample_candidates() -> List[Dict[str, Any]]:
+    return [
+        {
+            "exercise_id": 101,
+            "title": "Mae Geri — Stand und Hüftöffnung",
+            "summary": "Frontkick mit Hüftöffnung",
+            "skill_names": ["Geri Waza"],
+            "score_hint": 0.82,
+        },
+        {
+            "exercise_id": 102,
+            "title": "Mae Geri — Ballen und Rückzug",
+            "summary": "Ballentreffer mit Rückzug",
+            "skill_names": ["Geri Waza", "Koordination"],
+            "score_hint": 0.76,
+        },
+    ]
+
+
+def _load_catalog_variables(cur) -> Dict[str, str]:
+    from planning_exercise_intent import (
+        _load_compact_catalog,
+        _load_skills_catalog_compact,
+    )
+
+    return {
+        "skills_catalog_json": _compact_json(_load_skills_catalog_compact(cur)),
+        "focus_areas_catalog_json": _compact_json(_load_compact_catalog(cur, "focus_areas", "id")),
+        "training_types_catalog_json": _compact_json(_load_compact_catalog(cur, "training_types", "id")),
+        "style_directions_catalog_json": _compact_json(_load_compact_catalog(cur, "style_directions", "id")),
+        "target_groups_catalog_json": _compact_json(_load_compact_catalog(cur, "target_groups", "id")),
+    }
+
+
+def _preview_catalog_context(body: PlanningPromptPreviewInput):
+    from planning_catalog_context import catalog_context_from_mapping
+
+    raw = body.planning_catalog_context
+    if raw:
+        return catalog_context_from_mapping(raw)
+    return None
+
+
+def _merge_catalog_preview(cur, slug: str, base: Dict[str, str], body: PlanningPromptPreviewInput) -> Dict[str, str]:
+    return merge_planning_prompt_variables(
+        cur,
+        base,
+        catalog=_preview_catalog_context(body),
+        slug=slug,
+    )
+
+
+def resolve_planning_prompt_preview_variables(
+    cur,
+    slug: str,
+    body: PlanningPromptPreviewInput,
+) -> Dict[str, str]:
+    """Mustache-Variablen für Planungs-Prompt-Vorschau im Admin."""
+    s = (slug or "").strip().lower()
+    if s not in PLANNING_PROMPT_SLUGS:
+        raise ValueError(f"Kein Planungs-Prompt-Slug: {slug!r}")
+
+    goal_query = (body.goal_query or "").strip() or "Mae Geri Progression"
+    search_query = (body.search_query or "").strip() or goal_query
+    max_steps = int(body.max_steps)
+    brief = build_semantic_brief(goal_query)
+    brief_json = _compact_json(brief_to_summary_dict(brief))
+    goal_analysis = _sample_goal_analysis()
+    major_steps = _sample_major_steps(max_steps)
+    intent_ctx = build_planning_intent_context(
+        goal_query=goal_query,
+        goal_analysis=goal_analysis,
+        semantic_brief=brief,
+        extra_context=(body.user_notes or "").strip() or None,
+    )
+    intent_ctx_json = _compact_json(intent_ctx.to_api_dict())
+    ctx = _sample_planning_context()
+    target = _sample_target_profile()
+    catalogs = _load_catalog_variables(cur)
+
+    if s == "planning_progression_start_target":
+        return _merge_catalog_preview(
+            cur,
+            s,
+            {
+                "goal_query": goal_query,
+                "semantic_brief_json": brief_json,
+                "user_notes": (body.user_notes or "").strip(),
+            },
+            body,
+        )
+
+    if s == "planning_progression_goal_analysis":
+        return _merge_catalog_preview(
+            cur,
+            s,
+            {
+                "goal_query": goal_query,
+                "semantic_brief_json": brief_json,
+            },
+            body,
+        )
+
+    if s == "planning_progression_roadmap":
+        return _merge_catalog_preview(
+            cur,
+            s,
+            {
+                "goal_query": goal_query,
+                "goal_analysis_json": _compact_json(goal_analysis),
+                "semantic_brief_json": brief_json,
+                "max_steps": str(max_steps),
+            },
+            body,
+        )
+
+    if s == "planning_progression_stage_spec":
+        return _merge_catalog_preview(
+            cur,
+            s,
+            {
+                "goal_query": goal_query,
+                "goal_analysis_json": _compact_json(goal_analysis),
+                "major_steps_json": _compact_json(major_steps),
+                "intent_context_json": intent_ctx_json,
+                "semantic_brief_json": brief_json,
+            },
+            body,
+        )
+
+    if s == "planning_exercise_query_semantics":
+        return {
+            "search_query": search_query,
+            "semantic_brief_json": brief_json,
+        }
+
+    if s == "planning_exercise_path_qa":
+        return _merge_catalog_preview(
+            cur,
+            s,
+            {
+                "goal_query": goal_query,
+                "semantic_brief_json": brief_json,
+                "steps_json": _compact_json(_sample_path_steps()),
+                "gaps_json": _compact_json([]),
+                "bridge_inserts_json": _compact_json([]),
+            },
+            body,
+        )
+
+    if s == "planning_exercise_search_intent":
+        return {
+            "search_query": search_query,
+            "heuristic_intent": "progression_next",
+            "scenario_hint": "preset_next",
+            "planning_context_json": _compact_json(ctx),
+            "target_profile_json": _compact_json(target),
+            **catalogs,
+        }
+
+    if s == "planning_exercise_search_rank":
+        return {
+            "search_query": search_query,
+            "intent": "progression_next",
+            "planning_context_json": _compact_json(ctx),
+            "target_profile_json": _compact_json(target),
+            "candidates_json": _compact_json(_sample_candidates()),
+            "result_limit": "5",
+        }
+
+    if s == "planning_exercise_expectation_profile":
+        return {
+            "heuristic_intent": "suggest_next",
+            "planning_context_json": _compact_json(ctx),
+            "target_profile_json": _compact_json(target),
+            **{k: v for k, v in catalogs.items() if k != "style_directions_catalog_json"},
+        }
+
+    raise ValueError(f"Planungs-Prompt-Slug nicht implementiert: {slug!r}")
+
+
+__all__ = [
+    "PLANNING_PROMPT_SLUGS",
+    "PlanningPromptPreviewInput",
+    "is_planning_prompt_slug",
+    "resolve_planning_prompt_preview_variables",
+]

backend/catalog_prompt_slots.py

@@ -0,0 +1,432 @@
+"""
+Katalog-Prompt-Slots — Slot-Typ-Vokabular + Werte pro Stammdaten-Zeile (H2).
+
+Prompts in ai_prompts referenzieren Platzhalter wie {{focus_area_hints_on_progression}}.
+Inhalte liegen in catalog_prompt_slots (Admin-editierbar), nicht im Code pro Eintrag.
+"""
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple
+
+from planning_catalog_context import (
+    ProgressionPlanningCatalogContext,
+    PlanningCatalogContextItem,
+    catalog_context_has_items,
+)
+from catalog_slot_fallbacks import merge_stored_slots_with_fallbacks
+
+# ---------------------------------------------------------------------------
+# Dimensionen (Prioritätsreihenfolge)
+# ---------------------------------------------------------------------------
+
+@dataclass(frozen=True)
+class CatalogKindConfig:
+    kind: str
+    table: str
+    context_attr: str
+    label_de: str
+
+
+CATALOG_KINDS: Tuple[CatalogKindConfig, ...] = (
+    CatalogKindConfig("focus_area", "focus_areas", "focus_areas", "Primärfokus"),
+    CatalogKindConfig("training_type", "training_types", "training_types", "Trainingsstil"),
+    CatalogKindConfig("target_group", "target_groups", "target_groups", "Zielgruppe"),
+    CatalogKindConfig("style_direction", "style_directions", "style_directions", "Stilrichtung"),
+)
+
+_KIND_BY_NAME = {c.kind: c for c in CATALOG_KINDS}
+
+# ---------------------------------------------------------------------------
+# Slot-Typen (Vokabular — erweiterbar via catalog_prompt_slot_types)
+# ---------------------------------------------------------------------------
+
+SLOT_KEYS: Tuple[str, ...] = (
+    "description",
+    "hints_on_progression",
+    "hints_on_exercise",
+    "hints_on_path_qa",
+    "anti_patterns",
+    "rematch_guard",
+)
+
+LLM_SLOT_KEYS: Tuple[str, ...] = tuple(k for k in SLOT_KEYS if k != "rematch_guard")
+
+GUIDANCE_BLOCK_SLOTS: Tuple[str, ...] = (
+    "description",
+    "hints_on_progression",
+    "hints_on_path_qa",
+    "anti_patterns",
+)
+
+GUIDANCE_PROFILE_BY_SLUG: Dict[str, Tuple[str, ...]] = {
+    "planning_exercise_path_qa": ("description", "hints_on_path_qa", "anti_patterns"),
+    "planning_progression_roadmap": ("description", "hints_on_progression", "anti_patterns"),
+    "planning_progression_stage_spec": ("hints_on_progression", "anti_patterns", "description"),
+    "planning_progression_goal_analysis": ("description", "hints_on_progression"),
+    "planning_progression_start_target": ("description",),
+}
+
+
+def placeholder_key(catalog_kind: str, slot_key: str) -> str:
+    return f"{catalog_kind}_{slot_key}"
+
+
+def all_placeholder_keys() -> List[str]:
+    keys: List[str] = []
+    for cfg in CATALOG_KINDS:
+        for slot in SLOT_KEYS:
+            keys.append(placeholder_key(cfg.kind, slot))
+    keys.extend(["catalog_guidance_block", "catalog_context_json", "has_catalog_guidance"])
+    return keys
+
+
+def empty_catalog_variables() -> Dict[str, str]:
+    out = {k: "" for k in all_placeholder_keys()}
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Katalog-Kontext → aktiver Eintrag
+# ---------------------------------------------------------------------------
+
+
+def pick_active_catalog_item(
+    items: Sequence[PlanningCatalogContextItem],
+) -> Optional[PlanningCatalogContextItem]:
+    if not items:
+        return None
+    primaries = [i for i in items if i.is_primary]
+    if primaries:
+        return primaries[0]
+    if len(items) == 1:
+        return items[0]
+    return max(items, key=lambda i: (float(i.weight), -int(i.id)))
+
+
+def _load_catalog_row(cur, table: str, item_id: int) -> Optional[Dict[str, Any]]:
+    cur.execute(
+        f"""
+        SELECT id, name, description
+        FROM {table}
+        WHERE id = %s
+        """,
+        (int(item_id),),
+    )
+    row = cur.fetchone()
+    if not row:
+        return None
+    return {
+        "id": int(row["id"]),
+        "name": str(row.get("name") or "").strip(),
+        "description": str(row.get("description") or "").strip(),
+    }
+
+
+def _load_slots_for_entry(cur, catalog_kind: str, catalog_id: int) -> Dict[str, str]:
+    cur.execute(
+        """
+        SELECT slot_key, content
+        FROM catalog_prompt_slots
+        WHERE catalog_kind = %s AND catalog_id = %s
+        """,
+        (catalog_kind, int(catalog_id)),
+    )
+    out: Dict[str, str] = {}
+    for row in cur.fetchall():
+        key = str(row.get("slot_key") or "").strip()
+        if key:
+            out[key] = str(row.get("content") or "").strip()
+    return out
+
+
+def _slot_types_table_ready(cur) -> bool:
+    cur.execute("SELECT to_regclass(%s)::text AS t", ("public.catalog_prompt_slot_types",))
+    row = cur.fetchone()
+    if not row:
+        return False
+    val = row.get("t") if isinstance(row, dict) else row[0]
+    return val is not None and str(val).strip() != ""
+
+
+def list_slot_type_definitions(cur) -> List[Dict[str, Any]]:
+    if not _slot_types_table_ready(cur):
+        return _fallback_slot_type_rows()
+    cur.execute(
+        """
+        SELECT slot_key, display_name, description, applicable_kinds, sort_order, for_llm, for_code
+        FROM catalog_prompt_slot_types
+        ORDER BY sort_order ASC NULLS LAST, slot_key ASC
+        """
+    )
+    rows = []
+    for row in cur.fetchall():
+        d = dict(row)
+        kinds = d.get("applicable_kinds")
+        if isinstance(kinds, str):
+            kinds = [k.strip() for k in kinds.strip("{}").split(",") if k.strip()]
+        d["applicable_kinds"] = list(kinds or [])
+        rows.append(d)
+    return rows
+
+
+def _fallback_slot_type_rows() -> List[Dict[str, Any]]:
+    labels = {
+        "description": "Allgemeine Beschreibung",
+        "hints_on_progression": "Hinweise Progressionsgraph",
+        "hints_on_exercise": "Hinweise Übungsanlage",
+        "hints_on_path_qa": "Hinweise Pfad-QS",
+        "anti_patterns": "Anti-Patterns (Fehlbewertung vermeiden)",
+        "rematch_guard": "Rematch-Guard (Code)",
+    }
+    kinds = [c.kind for c in CATALOG_KINDS]
+    rows = []
+    for i, key in enumerate(SLOT_KEYS):
+        rows.append(
+            {
+                "slot_key": key,
+                "display_name": labels.get(key, key),
+                "description": "",
+                "applicable_kinds": kinds,
+                "sort_order": (i + 1) * 10,
+                "for_llm": key != "rematch_guard",
+                "for_code": key == "rematch_guard",
+            }
+        )
+    return rows
+
+
+def _resolve_entry_slot_values(
+    stored: Mapping[str, str],
+    row: Mapping[str, Any],
+    catalog_kind: str,
+) -> Dict[str, str]:
+    """DB → Namens-Fallback → Stammdaten-Beschreibung (nur description)."""
+    return merge_stored_slots_with_fallbacks(
+        stored,
+        catalog_kind=catalog_kind,
+        name=str(row.get("name") or ""),
+        stammdaten_description=str(row.get("description") or ""),
+    )
+
+
+def get_catalog_entry_slots(cur, catalog_kind: str, catalog_id: int) -> Dict[str, Any]:
+    cfg = _KIND_BY_NAME.get((catalog_kind or "").strip())
+    if not cfg:
+        raise ValueError(f"Unbekannter catalog_kind: {catalog_kind!r}")
+    row = _load_catalog_row(cur, cfg.table, catalog_id)
+    if not row:
+        raise LookupError("Katalog-Eintrag nicht gefunden")
+    stored = _load_slots_for_entry(cur, cfg.kind, catalog_id)
+    merged = _resolve_entry_slot_values(stored, row, cfg.kind)
+    return {
+        "catalog_kind": cfg.kind,
+        "catalog_id": int(catalog_id),
+        "name": row["name"],
+        "slots": merged,
+        "stored_slots": {k: stored.get(k, "") for k in SLOT_KEYS},
+    }
+
+
+def upsert_catalog_entry_slots(
+    cur,
+    catalog_kind: str,
+    catalog_id: int,
+    slots: Mapping[str, Any],
+) -> Dict[str, Any]:
+    cfg = _KIND_BY_NAME.get((catalog_kind or "").strip())
+    if not cfg:
+        raise ValueError(f"Unbekannter catalog_kind: {catalog_kind!r}")
+    row = _load_catalog_row(cur, cfg.table, catalog_id)
+    if not row:
+        raise LookupError("Katalog-Eintrag nicht gefunden")
+    for slot_key, raw in (slots or {}).items():
+        sk = str(slot_key or "").strip()
+        if sk not in SLOT_KEYS:
+            continue
+        content = str(raw or "").strip()
+        if not content:
+            cur.execute(
+                """
+                DELETE FROM catalog_prompt_slots
+                WHERE catalog_kind = %s AND catalog_id = %s AND slot_key = %s
+                """,
+                (cfg.kind, int(catalog_id), sk),
+            )
+            continue
+        cur.execute(
+            """
+            INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content, updated_at)
+            VALUES (%s, %s, %s, %s, NOW())
+            ON CONFLICT (catalog_kind, catalog_id, slot_key)
+            DO UPDATE SET content = EXCLUDED.content, updated_at = NOW()
+            """,
+            (cfg.kind, int(catalog_id), sk, content),
+        )
+    return get_catalog_entry_slots(cur, cfg.kind, catalog_id)
+
+
+def _render_dimension_section(
+    label_de: str,
+    name: str,
+    slot_values: Mapping[str, str],
+    *,
+    slot_keys: Sequence[str],
+) -> Optional[str]:
+    parts: List[str] = [f"### {label_de} — {name}"]
+    labels = {
+        "description": "Beschreibung",
+        "hints_on_progression": "Progressions-Hinweise",
+        "hints_on_path_qa": "QS-Hinweise",
+        "hints_on_exercise": "Übungsanlage",
+        "anti_patterns": "Vermeiden",
+    }
+    added = False
+    for sk in slot_keys:
+        text = str(slot_values.get(sk) or "").strip()
+        if not text:
+            continue
+        added = True
+        if sk == "description":
+            parts.append(text)
+        else:
+            parts.append(f"{labels.get(sk, sk)}: {text}")
+    if not added:
+        return None
+    return "\n".join(parts)
+
+
+def _compose_guidance_block(
+    sections: List[str],
+) -> str:
+    if not sections:
+        return ""
+    return "## Katalog-Kontext (Didaktik & Bewertung)\n\n" + "\n\n".join(sections)
+
+
+def resolve_catalog_prompt_variables(
+    cur,
+    catalog: Optional[ProgressionPlanningCatalogContext],
+    *,
+    slug: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Liefert Mustache-Strings + Metadaten.
+
+    Returns dict mit allen {{kind_slot}} Keys, catalog_guidance_block, catalog_context_json,
+    has_catalog_guidance (bool), active_slots (list).
+    """
+    variables = empty_catalog_variables()
+    meta: Dict[str, Any] = {
+        "active_slots": [],
+        "audit": {},
+    }
+    if cur is None or not catalog_context_has_items(catalog):
+        variables["catalog_context_json"] = ""
+        return {**variables, **meta}
+
+    profile = GUIDANCE_PROFILE_BY_SLUG.get((slug or "").strip().lower(), GUIDANCE_BLOCK_SLOTS)
+    sections: List[str] = []
+    audit: Dict[str, Any] = {}
+    has_any = False
+    active_slots: List[str] = []
+
+    for cfg in CATALOG_KINDS:
+        items = getattr(catalog, cfg.context_attr, None) or []
+        active = pick_active_catalog_item(items)
+        if not active:
+            continue
+        row = _load_catalog_row(cur, cfg.table, active.id)
+        if not row:
+            continue
+        stored = _load_slots_for_entry(cur, cfg.kind, row["id"]) if _slot_types_table_ready(cur) else {}
+        slot_values = _resolve_entry_slot_values(stored, row, cfg.kind)
+        for sk in SLOT_KEYS:
+            pk = placeholder_key(cfg.kind, sk)
+            text = slot_values.get(sk, "")
+            variables[pk] = text
+            if text.strip() and sk in LLM_SLOT_KEYS:
+                has_any = True
+                active_slots.append(pk)
+
+        audit[cfg.context_attr] = {
+            "catalog_kind": cfg.kind,
+            "id": row["id"],
+            "name": row["name"],
+            "is_primary": bool(active.is_primary),
+            "weight": float(active.weight),
+            "filled_slots": [k for k in LLM_SLOT_KEYS if slot_values.get(k, "").strip()],
+            "stored_slots": [k for k in SLOT_KEYS if (stored.get(k) or "").strip()],
+        }
+
+        section = _render_dimension_section(cfg.label_de, row["name"], slot_values, slot_keys=profile)
+        if section:
+            sections.append(section)
+
+    variables["catalog_guidance_block"] = _compose_guidance_block(sections)
+    ctx_json = json.dumps(audit, ensure_ascii=False, separators=(",", ":"))
+    variables["catalog_context_json"] = f"Katalog-Audit: {ctx_json}" if audit else ""
+    variables["has_catalog_guidance"] = "true" if has_any else ""
+    return {
+        **variables,
+        "active_slots": active_slots,
+        "audit": audit,
+    }
+
+
+def get_rematch_guard_for_catalog(
+    cur,
+    catalog: Optional[ProgressionPlanningCatalogContext],
+) -> Optional[str]:
+    """Erste passende rematch_guard entlang der Dimensions-Priorität."""
+    if cur is None or not catalog_context_has_items(catalog):
+        return None
+    for cfg in CATALOG_KINDS:
+        items = getattr(catalog, cfg.context_attr, None) or []
+        active = pick_active_catalog_item(items)
+        if not active:
+            continue
+        stored = _load_slots_for_entry(cur, cfg.kind, active.id)
+        row = _load_catalog_row(cur, cfg.table, active.id)
+        if not row:
+            continue
+        slot_values = _resolve_entry_slot_values(stored, row, cfg.kind)
+        guard = (slot_values.get("rematch_guard") or "").strip()
+        if guard:
+            return guard
+    return None
+
+
+# Abwärtskompatibilität H1-API
+def build_catalog_guidance_for_prompt(
+    cur,
+    catalog: Optional[ProgressionPlanningCatalogContext],
+    *,
+    slug: Optional[str] = None,
+) -> Dict[str, Any]:
+    resolved = resolve_catalog_prompt_variables(cur, catalog, slug=slug)
+    return {
+        "catalog_guidance_block": resolved.get("catalog_guidance_block", ""),
+        "catalog_context_json": resolved.get("catalog_context_json", ""),
+        "has_catalog_guidance": resolved.get("has_catalog_guidance") == "true",
+        "snippet_keys": list(resolved.get("active_slots") or []),
+        "variables": {k: str(resolved.get(k) or "") for k in all_placeholder_keys()},
+    }
+
+
+__all__ = [
+    "CATALOG_KINDS",
+    "GUIDANCE_PROFILE_BY_SLUG",
+    "SLOT_KEYS",
+    "build_catalog_guidance_for_prompt",
+    "empty_catalog_variables",
+    "get_catalog_entry_slots",
+    "get_rematch_guard_for_catalog",
+    "list_slot_type_definitions",
+    "pick_active_catalog_item",
+    "placeholder_key",
+    "all_placeholder_keys",
+    "resolve_catalog_prompt_variables",
+    "upsert_catalog_entry_slots",
+]

backend/catalog_slot_fallbacks.py

@@ -0,0 +1,284 @@
+"""
+Namensbasierte Fallback-Slots — bis Admin/DB befüllt sind (H1-Registry-Inhalt).
+
+DB-Werte in catalog_prompt_slots haben immer Vorrang. Fallbacks füllen nur leere Slot-Keys.
+"""
+from __future__ import annotations
+
+import re
+import unicodedata
+from typing import Dict, Mapping, Optional, Sequence, Tuple
+
+_UMLAUT_MAP = str.maketrans({"ä": "ae", "ö": "oe", "ü": "ue", "ß": "ss", "Ä": "ae", "Ö": "oe", "Ü": "ue"})
+
+SlotPack = Dict[str, str]
+
+# (catalog_kind, name_pattern_lower) — erste passende Regel gewinnt; * = Default pro Kind
+_FALLBACK_RULES: Tuple[Tuple[str, str, SlotPack], ...] = (
+    # --- focus_area ---
+    (
+        "focus_area",
+        "gewaltschutz",
+        {
+            "description": (
+                "Planung zielt auf Prävention, Deeskalation, Grenzen und sichere Übungsformen — "
+                "nicht auf Wettkampf-Perfektion oder Technik-Show."
+            ),
+            "hints_on_progression": (
+                "Phasen: Wahrnehmung → Grenzen → Deeskalation → sichere Übungsformen; "
+                "keine Kumite-Perfektionsstufen erzwingen."
+            ),
+            "hints_on_exercise": (
+                "Übungen mit Rollen, Kommunikation, Ausweichen; keine rein technischen Kick-Fokus-Inseln ohne Bezug."
+            ),
+            "hints_on_path_qa": (
+                "Gute Pfade bauen Sicherheit, Kommunikation und Alternativen auf; "
+                "„Lücken“ sind fehlende Deeskalations- oder Rollenspiel-Stufen, nicht fehlende Kick-Varianten."
+            ),
+            "anti_patterns": "Nicht nach Kumite-Tiefe, Explosivität oder Wettkampf-Belastung bewerten.",
+        },
+    ),
+    (
+        "focus_area",
+        "selbstverteidigung",
+        {
+            "description": (
+                "Praktische Selbstverteidigung: realistische Szenarien, Sicherheit und "
+                "anwendungsnahe Progression — nicht Show-Technik oder Wettkampf-Kata."
+            ),
+            "hints_on_progression": (
+                "Von Wahrnehmung und Distanz zu einfachen Abwehrmustern und kontrollierter Anwendung."
+            ),
+            "hints_on_exercise": "Partnerübungen mit klaren Sicherheitsregeln; Szenario-Bezug wichtiger als Stil-Show.",
+            "hints_on_path_qa": (
+                "Lücken bei Szenario- oder Sicherheitsstufen sind relevant; "
+                "fehlende Kick-Varianten oder Wettkampftiefe sind kein Mangel."
+            ),
+            "anti_patterns": "Keine Wettkampf- oder Kata-Perfektion als QS-Maßstab.",
+        },
+    ),
+    (
+        "focus_area",
+        "fitness",
+        {
+            "description": (
+                "Fitness- und Konditionsorientierung mit sicherer Belastungssteuerung; "
+                "Technikbezug nur wo fachlich sinnvoll."
+            ),
+            "hints_on_progression": "Progression von niedriger zu moderater Belastung; klare Pausen und Technikhygiene.",
+            "hints_on_path_qa": (
+                "Keine Wettkampf-Spezialisierung als Pflicht-Kriterium; "
+                "Belastungssteigerung ohne Technikbezug abwerten."
+            ),
+            "anti_patterns": "Keine Kumite-Perfektion oder Wettkampf-Kombinationen als QS-Maßstab verlangen.",
+        },
+    ),
+    (
+        "focus_area",
+        "karate",
+        {
+            "description": (
+                "Technik-Curriculum im Karate-Kontext: aufeinander aufbauende Kihon-Progression "
+                "mit klaren Qualitätsankern (Stand, Hüfte, Kime)."
+            ),
+            "hints_on_progression": (
+                "Typische Phasen: Einstieg → Grundlagen → Koordination/Kraft → Anwendung → optional Vertiefung; "
+                "Grundlagen vor Perfektion."
+            ),
+            "hints_on_exercise": (
+                "Kihon und Partnerübungen mit Technikbezug; reine Kraft-/Ausdauer-Inseln nur mit klarer Begründung."
+            ),
+            "hints_on_path_qa": (
+                "Kohärente Progression Grundlagen → Anwendung → Vertiefung; "
+                "Übergänge ohne Sprünge; themenfremde Kraft-/Ausdauer-Inseln abwerten."
+            ),
+            "anti_patterns": "Keine pauschale Perfektions-Stufe verlangen, wenn Kontext Breitensport ist.",
+        },
+    ),
+    (
+        "focus_area",
+        "*",
+        {
+            "description": "Technik- oder Themen-Curriculum mit didaktisch aufeinander aufbauenden Stufen.",
+            "hints_on_progression": "Grundlagen vor Anwendung; moderate Sprünge zwischen Stufen vermeiden.",
+            "hints_on_path_qa": (
+                "Kohärente Progression zum Anfrage-Thema; "
+                "Lücken sind fehlende Zwischenstufen im Lernpfad, nicht fehlende Nebenthemen."
+            ),
+            "hints_on_exercise": "Übungen mit klarem Bezug zum Pfad-Thema und zur Stufe.",
+        },
+    ),
+    # --- training_type ---
+    (
+        "training_type",
+        "breitensport",
+        {
+            "description": (
+                "Partizipation, Verständlichkeit, Freude am Bewegen; weniger maximale Spezialisierung."
+            ),
+            "hints_on_progression": "Moderater Schwierigkeitsanstieg; Perfektionsphasen optional.",
+            "hints_on_path_qa": (
+                "Hohe OK-Rate bei moderatem Schwierigkeitsanstieg; "
+                "„Perfektion“-Stufen nur optional, nicht als Pflicht-Lücke."
+            ),
+            "rematch_guard": "Keine leeren Slots erzwingen, nur um eine Leistungs-Perfektionsstufe zu füllen.",
+        },
+    ),
+    (
+        "training_type",
+        "leistungssport",
+        {
+            "description": "Leistungsorientiertes Training mit höherer Anspruchskurve und Spezialisierung.",
+            "hints_on_progression": "Belastungs- und Kombinationsprogressionen sind erwünscht.",
+            "hints_on_path_qa": (
+                "Höhere Anspruchskurven sind passend; Lücken in Spezialisierung können echte Hinweise sein."
+            ),
+        },
+    ),
+    (
+        "training_type",
+        "wettkampf",
+        {
+            "description": (
+                "Wettkampforientiertes Training mit höherer Anspruchskurve und belastungsnahen Phasen."
+            ),
+            "hints_on_progression": "Anwendungs- und Druckphasen zeitig einplanen.",
+            "hints_on_path_qa": (
+                "Spezialisierung, Kombination und Belastung unter Druck sind relevant; "
+                "Lücken in Anwendungs- oder Perfektionsphasen können echte Hinweise sein."
+            ),
+        },
+    ),
+    (
+        "training_type",
+        "*",
+        {
+            "hints_on_path_qa": "Didaktische Kohärenz wichtiger als maximale Spezialisierung — Kontext beachten.",
+        },
+    ),
+    # --- target_group ---
+    (
+        "target_group",
+        "kinder",
+        {
+            "description": (
+                "Kinder: kurze Einheiten, spielerische Einstiege, Sicherheit und altersgerechte Komplexität."
+            ),
+            "hints_on_progression": "Spielerische Einstiege; kurze Abschnitte; Sicherheit vor Perfektion.",
+            "hints_on_path_qa": (
+                "Didaktik ohne Überforderung; klare Regeln und Sicherheit vor Perfektion; "
+                "Lücken bei Spiel-/Rollenelementen wichtiger als Wettkampftiefe."
+            ),
+            "anti_patterns": "Keine Erwachsenen-Wettkampf-Perfektion als QS-Maßstab.",
+        },
+    ),
+    (
+        "target_group",
+        "leistungssportler",
+        {
+            "description": "Leistungsgruppe: höhere Anspruchskurven und Spezialisierung sind fachlich passend.",
+            "hints_on_progression": "Anspruchskurve und Spezialisierung dürfen steiler sein.",
+            "hints_on_path_qa": (
+                "Höhere Anspruchskurven, Belastungs- und Kombinationsprogressionen sind relevant; "
+                "Lücken in Spezialisierung können echte Hinweise sein."
+            ),
+        },
+    ),
+    (
+        "target_group",
+        "breitensportler",
+        {
+            "description": "Breitensport: Partizipation und Verständlichkeit vor maximaler Spezialisierung.",
+            "hints_on_path_qa": (
+                "Moderate Progression; Perfektions-Lücken sind selten echte Mängel."
+            ),
+            "anti_patterns": "Keine Leistungssport-Perfektion als Pflicht-Kriterium.",
+        },
+    ),
+    (
+        "target_group",
+        "*",
+        {
+            "hints_on_path_qa": "Zielgruppe im Tempo und in der Komplexität berücksichtigen.",
+        },
+    ),
+    # --- style_direction ---
+    (
+        "style_direction",
+        "shotokan",
+        {
+            "description": (
+                "Shotokan-Linie: klare Kihon-Struktur, Hüft- und Standarbeit als wiederkehrende Qualitätsanker."
+            ),
+            "hints_on_progression": "Nuancen in Stellung und Hüfttechnik; kein neuer Planungstyp.",
+            "hints_on_path_qa": "Konsistenz von Stand, Hüfte und Kime entlang des Pfads bewerten.",
+        },
+    ),
+    (
+        "style_direction",
+        "*",
+        {
+            "hints_on_progression": (
+                "Stil-spezifische Nuancen (Stand, Hüfte, Rhythmus) einbeziehen — ohne Stilwechsel zu erzwingen."
+            ),
+        },
+    ),
+)
+
+
+def normalize_catalog_name_key(name: str) -> str:
+    s = unicodedata.normalize("NFKD", (name or "").translate(_UMLAUT_MAP))
+    s = s.encode("ascii", "ignore").decode("ascii").lower()
+    s = re.sub(r"[^a-z0-9]+", "_", s).strip("_")
+    return s or "unknown"
+
+
+def get_fallback_slots_for_entry(catalog_kind: str, name: str) -> SlotPack:
+    kind = (catalog_kind or "").strip().lower()
+    norm = normalize_catalog_name_key(name)
+    default: SlotPack = {}
+    for rule_kind, pattern, pack in _FALLBACK_RULES:
+        if rule_kind != kind:
+            continue
+        if pattern == "*":
+            default = dict(pack)
+            continue
+        if pattern in norm or norm.startswith(pattern) or pattern in (name or "").lower():
+            return dict(pack)
+    return default
+
+
+def merge_stored_slots_with_fallbacks(
+    stored: Mapping[str, str],
+    *,
+    catalog_kind: str,
+    name: str,
+    stammdaten_description: str = "",
+) -> Dict[str, str]:
+    """DB + Stammdaten-Beschreibung + Namens-Fallback."""
+    fallbacks = get_fallback_slots_for_entry(catalog_kind, name)
+    out: Dict[str, str] = {}
+    for key in (
+        "description",
+        "hints_on_progression",
+        "hints_on_exercise",
+        "hints_on_path_qa",
+        "anti_patterns",
+        "rematch_guard",
+    ):
+        if key == "description":
+            out[key] = (
+                (stored.get(key) or "").strip()
+                or (fallbacks.get(key) or "").strip()
+                or (stammdaten_description or "").strip()
+            )
+        else:
+            out[key] = (stored.get(key) or "").strip() or (fallbacks.get(key) or "").strip()
+    return out
+
+
+__all__ = [
+    "get_fallback_slots_for_entry",
+    "merge_stored_slots_with_fallbacks",
+    "normalize_catalog_name_key",
+]

backend/main.py

@@ -243,7 +243,7 @@ def read_root():
     return out
 
 # Register routers
-from routers import auth, profiles, exercises, exercise_progression_graphs, clubs, club_memberships, club_join_requests, club_creation_requests, admin_users, admin_user_content, admin_rights, me_entitlements, platform_media_storage, media_assets, skills, skill_profiles, training_planning, planning_exercise_suggest, dashboard, training_modules, training_framework_programs, catalogs, maturity_models, matrix_stack_bundle, matrix_editor, import_wiki, import_wiki_admin, legal_documents, content_reports, ai_prompts_admin, ai_skill_retrieval_admin, exercise_enrichment_admin
+from routers import auth, profiles, exercises, exercise_progression_graphs, clubs, club_memberships, club_join_requests, club_creation_requests, admin_users, admin_user_content, admin_rights, me_entitlements, platform_media_storage, media_assets, skills, skill_profiles, training_planning, planning_exercise_suggest, dashboard, training_modules, training_framework_programs, catalogs, catalog_prompt_slots, maturity_models, matrix_stack_bundle, matrix_editor, import_wiki, import_wiki_admin, legal_documents, content_reports, ai_prompts_admin, ai_skill_retrieval_admin, exercise_enrichment_admin
 
 app.include_router(auth.router)
 app.include_router(profiles.router)
@@ -269,6 +269,7 @@ app.include_router(dashboard.router)
 app.include_router(training_modules.router)
 app.include_router(training_framework_programs.router)
 app.include_router(catalogs.router)
+app.include_router(catalog_prompt_slots.router)
 app.include_router(maturity_models.router)
 app.include_router(matrix_stack_bundle.router)
 app.include_router(matrix_editor.router)

backend/migrations/091_ai_prompt_catalog_guidance.sql

@@ -0,0 +1,172 @@
+-- Migration 091: Planungs-KI H1 — Katalog-Guidance-Platzhalter in Progressions-Prompts
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und prüfst einen vorgeschlagenen Übungspfad.
+
+Ziel-Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+Schritte (JSON): {{steps_json}}
+Erkannte Lücken: {{gaps_json}}
+Eingefügte Brücken: {{bridge_inserts_json}}
+
+{{catalog_guidance_block}}
+{{catalog_context_json}}
+
+Wichtig: Wenn Katalog-Kontext gesetzt ist, haben dessen QS-Kriterien Vorrang vor allgemeinen Technik-/Wettkampf-Maßstäben.
+
+Prüfe:
+1. Deckt der Pfad das Hauptthema der Anfrage ab (nicht nur Oberbegriffe)?
+2. Ist die Reihenfolge didaktisch sinnvoll (Einstieg → Vertiefung → Ziel)?
+3. Sind Sprünge zwischen benachbarten Schritten zu groß?
+4. Sind Brücken-Übungen sinnvoll oder überflüssig?
+5. Fehlen wichtige Zwischenschritte (Kraft, Geschwindigkeit, Anwendung, Perfektion)?
+6. Gibt es Schritte ohne Bezug zum Hauptthema (z. B. reine Kraftübungen bei einer Technik)?
+
+Wenn die Reihenfolge verbessert werden sollte: ordered_step_indices = Permutation der aktuellen 0-basierten Schritt-Indizes (beste didaktische Reihenfolge).
+Nur Indizes aus dem steps_json verwenden — Länge muss exakt der Schrittzahl entsprechen.
+
+Wenn wichtige Zwischenschritte fehlen oder Schritte themenfremd sind: suggested_new_exercises mit konkreten Übungs-Ideen (Titel + Kurzskizze), jeweils mit insert_after_step_index (0-basiert: nach welchem Schritt einfügen).
+
+Antworte NUR mit JSON:
+{
+  "overall_ok": true,
+  "quality_score": 0.85,
+  "topic_coverage": "Kurz: wie gut das Hauptthema abgedeckt ist",
+  "ordered_step_indices": [0, 1, 2, 3],
+  "issues": ["…"],
+  "sequence_notes": ["…"],
+  "recommendations": ["…"],
+  "suggested_new_exercises": [
+    {
+      "title_hint": "Mae Geri Kraftentwicklung am Sandsack",
+      "sketch": "Gezielte Kraft- und Schnelligkeitsentwicklung für Mae Geri …",
+      "phase": "vertiefung",
+      "insert_after_step_index": 2,
+      "rationale": "Schließt Lücke zwischen Grundlagen und Gleichgewichtstritt"
+    }
+  ]
+}$t$,
+    default_template = $t$Du bist Assistent für Kampfsport-Trainer und prüfst einen vorgeschlagenen Übungspfad.
+
+Ziel-Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+Schritte (JSON): {{steps_json}}
+Erkannte Lücken: {{gaps_json}}
+Eingefügte Brücken: {{bridge_inserts_json}}
+
+{{catalog_guidance_block}}
+{{catalog_context_json}}
+
+Wichtig: Wenn Katalog-Kontext gesetzt ist, haben dessen QS-Kriterien Vorrang vor allgemeinen Technik-/Wettkampf-Maßstäben.
+
+Prüfe:
+1. Deckt der Pfad das Hauptthema der Anfrage ab (nicht nur Oberbegriffe)?
+2. Ist die Reihenfolge didaktisch sinnvoll (Einstieg → Vertiefung → Ziel)?
+3. Sind Sprünge zwischen benachbarten Schritten zu groß?
+4. Sind Brücken-Übungen sinnvoll oder überflüssig?
+5. Fehlen wichtige Zwischenschritte (Kraft, Geschwindigkeit, Anwendung, Perfektion)?
+6. Gibt es Schritte ohne Bezug zum Hauptthema (z. B. reine Kraftübungen bei einer Technik)?
+
+Wenn die Reihenfolge verbessert werden sollte: ordered_step_indices = Permutation der aktuellen 0-basierten Schritt-Indizes (beste didaktische Reihenfolge).
+Nur Indizes aus dem steps_json verwenden — Länge muss exakt der Schrittzahl entsprechen.
+
+Wenn wichtige Zwischenschritte fehlen oder Schritte themenfremd sind: suggested_new_exercises mit konkreten Übungs-Ideen (Titel + Kurzskizze), jeweils mit insert_after_step_index (0-basiert: nach welchem Schritt einfügen).
+
+Antworte NUR mit JSON:
+{
+  "overall_ok": true,
+  "quality_score": 0.85,
+  "topic_coverage": "Kurz: wie gut das Hauptthema abgedeckt ist",
+  "ordered_step_indices": [0, 1, 2, 3],
+  "issues": ["…"],
+  "sequence_notes": ["…"],
+  "recommendations": ["…"],
+  "suggested_new_exercises": [
+    {
+      "title_hint": "Mae Geri Kraftentwicklung am Sandsack",
+      "sketch": "Gezielte Kraft- und Schnelligkeitsentwicklung für Mae Geri …",
+      "phase": "vertiefung",
+      "insert_after_step_index": 2,
+      "rationale": "Schließt Lücke zwischen Grundlagen und Gleichgewichtstritt"
+    }
+  ]
+}$t$
+WHERE slug = 'planning_exercise_path_qa';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und analysierst eine Anfrage für einen Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+
+{{catalog_guidance_block}}
+
+Wichtig: Keine Gruppenanalyse — nur didaktischer Pfad für die Technik/das Thema.
+
+Antworte NUR mit JSON:
+{
+  "primary_topic": "Mae Geri",
+  "start_assumption": "Welche Voraussetzungen werden für den Einstieg angenommen",
+  "target_state": "Konkreter Zielzustand der Progression",
+  "success_criteria": ["messbare Kriterien"],
+  "constraints": { "partner_required": false }
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_goal_analysis';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und erstellst eine didaktische Roadmap für einen Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Zielanalyse: {{goal_analysis_json}}
+Semantic Brief: {{semantic_brief_json}}
+Anzahl Major Steps (N): {{max_steps}}
+
+{{catalog_guidance_block}}
+{{catalog_context_json}}
+
+Erzeuge zuerst 8–12 micro_objectives (phase, title, weight, depends_on), dann konsolidiere auf genau N major_steps.
+Phasen: einstieg, grundlage, vertiefung, anwendung, perfektion — in sinnvoller Reihenfolge (Grundlagen vor Perfektion).
+Beachte Katalog-Roadmap-Hinweise, falls gesetzt.
+
+Antworte NUR mit JSON:
+{
+  "micro_objectives": [
+    { "id": "m1", "phase": "grundlage", "title": "…", "weight": 0.9, "depends_on": [] }
+  ],
+  "major_steps": [
+    { "index": 0, "phase": "grundlage", "learning_goal": "…", "consolidates": ["m1","m2"], "rationale": "…" }
+  ],
+  "consolidation_notes": ["…"]
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_roadmap';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und spezifizierst didaktische Stufen eines Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Zielanalyse: {{goal_analysis_json}}
+Major Steps: {{major_steps_json}}
+
+{{catalog_guidance_block}}
+{{catalog_context_json}}
+
+Für jeden Major Step: messbares Lernziel, load_profile (z. B. koordination, präzision, kraft), exercise_type (kihon_einzel, partner_drill, kombination, kraft_auxiliary), success_criteria, anti_patterns (z. B. reine Kraft ohne Technikbezug).
+Beachte Katalog-QS-Kriterien und Anti-Patterns, falls gesetzt.
+
+Antworte NUR mit JSON:
+{
+  "stage_specs": [
+    {
+      "major_step_index": 0,
+      "learning_goal": "…",
+      "load_profile": ["koordination", "gleichgewicht"],
+      "exercise_type": "kihon_einzel",
+      "success_criteria": ["…"],
+      "anti_patterns": ["…"]
+    }
+  ]
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_stage_spec';

backend/migrations/092_catalog_prompt_slots.sql

@@ -0,0 +1,176 @@
+-- Migration 092: Katalog-Prompt-Slots (H2) — Slot-Typ-Vokabular + Werte pro Stammdaten-Zeile
+
+CREATE TABLE IF NOT EXISTS catalog_prompt_slot_types (
+    slot_key VARCHAR(64) PRIMARY KEY,
+    display_name VARCHAR(200) NOT NULL,
+    description TEXT,
+    applicable_kinds TEXT[] NOT NULL DEFAULT '{}',
+    sort_order INT DEFAULT 99,
+    for_llm BOOLEAN NOT NULL DEFAULT true,
+    for_code BOOLEAN NOT NULL DEFAULT false,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+
+CREATE TABLE IF NOT EXISTS catalog_prompt_slots (
+    id SERIAL PRIMARY KEY,
+    catalog_kind VARCHAR(32) NOT NULL,
+    catalog_id INT NOT NULL,
+    slot_key VARCHAR(64) NOT NULL REFERENCES catalog_prompt_slot_types(slot_key) ON DELETE CASCADE,
+    content TEXT NOT NULL DEFAULT '',
+    updated_at TIMESTAMP DEFAULT NOW(),
+    UNIQUE (catalog_kind, catalog_id, slot_key)
+);
+
+CREATE INDEX IF NOT EXISTS idx_catalog_prompt_slots_kind_id
+    ON catalog_prompt_slots (catalog_kind, catalog_id);
+
+INSERT INTO catalog_prompt_slot_types (slot_key, display_name, description, applicable_kinds, sort_order, for_llm, for_code)
+VALUES
+    (
+        'description',
+        'Allgemeine Beschreibung',
+        'Fachliche Einordnung des Katalog-Eintrags für Planungs-KI.',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        10,
+        true,
+        false
+    ),
+    (
+        'hints_on_progression',
+        'Hinweise Progressionsgraph',
+        'Didaktik für Roadmap, Major Steps und Stufenspezifikation.',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        20,
+        true,
+        false
+    ),
+    (
+        'hints_on_exercise',
+        'Hinweise Übungsanlage',
+        'Kontext für Gap-Fill, Übungs-KI und Schnellanlage.',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        30,
+        true,
+        false
+    ),
+    (
+        'hints_on_path_qa',
+        'Hinweise Pfad-QS',
+        'Bewertungsmaßstäbe für Pfad-Qualitätssicherung.',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        40,
+        true,
+        false
+    ),
+    (
+        'anti_patterns',
+        'Anti-Patterns',
+        'Explizite Fehlbewertungen vermeiden.',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        50,
+        true,
+        false
+    ),
+    (
+        'rematch_guard',
+        'Rematch-Guard',
+        'Wann kein Auto-Rematch sinnvoll ist (primär Code-Logik).',
+        ARRAY['focus_area', 'training_type', 'target_group', 'style_direction'],
+        60,
+        false,
+        true
+    )
+ON CONFLICT (slot_key) DO NOTHING;
+
+-- Seed aus H1-Registry (Name-Match auf Stammdaten)
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'focus_area', fa.id, 'description',
+    'Planung zielt auf Prävention, Deeskalation, Grenzen und sichere Übungsformen — nicht auf Wettkampf-Perfektion oder Technik-Show.'
+FROM focus_areas fa WHERE fa.name ILIKE 'Gewaltschutz'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'focus_area', fa.id, 'hints_on_path_qa',
+    'Gute Pfade bauen Sicherheit, Kommunikation und Alternativen auf; „Lücken“ sind fehlende Deeskalations- oder Rollenspiel-Stufen, nicht fehlende Kick-Varianten.'
+FROM focus_areas fa WHERE fa.name ILIKE 'Gewaltschutz'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'focus_area', fa.id, 'anti_patterns',
+    'Nicht nach Kumite-Tiefe, Explosivität oder Wettkampf-Belastung bewerten.'
+FROM focus_areas fa WHERE fa.name ILIKE 'Gewaltschutz'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'training_type', tt.id, 'description',
+    'Partizipation, Verständlichkeit, Freude am Bewegen; weniger maximale Spezialisierung.'
+FROM training_types tt WHERE tt.name ILIKE 'Breitensport'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'training_type', tt.id, 'hints_on_path_qa',
+    'Hohe OK-Rate bei moderatem Schwierigkeitsanstieg; „Perfektion“-Stufen nur optional, nicht als Pflicht-Lücke.'
+FROM training_types tt WHERE tt.name ILIKE 'Breitensport'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'training_type', tt.id, 'rematch_guard',
+    'Keine leeren Slots erzwingen, nur um eine Leistungs-Perfektionsstufe zu füllen.'
+FROM training_types tt WHERE tt.name ILIKE 'Breitensport'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'target_group', tg.id, 'description',
+    'Kinder: kurze Einheiten, spielerische Einstiege, Sicherheit und altersgerechte Komplexität.'
+FROM target_groups tg WHERE tg.name ILIKE 'Kinder'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'target_group', tg.id, 'hints_on_path_qa',
+    'Didaktik ohne Überforderung; klare Regeln und Sicherheit vor Perfektion; Lücken bei Spiel-/Rollenelementen wichtiger als Wettkampftiefe.'
+FROM target_groups tg WHERE tg.name ILIKE 'Kinder'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'target_group', tg.id, 'anti_patterns',
+    'Keine Erwachsenen-Wettkampf-Perfektion als QS-Maßstab.'
+FROM target_groups tg WHERE tg.name ILIKE 'Kinder'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'target_group', tg.id, 'description',
+    'Leistungsgruppe: höhere Anspruchskurven und Spezialisierung sind fachlich passend.'
+FROM target_groups tg WHERE tg.name ILIKE 'Leistungssportler'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'target_group', tg.id, 'hints_on_path_qa',
+    'Höhere Anspruchskurven, Belastungs- und Kombinationsprogressionen sind relevant; Lücken in Spezialisierung können echte Hinweise sein.'
+FROM target_groups tg WHERE tg.name ILIKE 'Leistungssportler'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'style_direction', sd.id, 'description',
+    'Shotokan-Linie: klare Kihon-Struktur, Hüft- und Standarbeit als wiederkehrende Qualitätsanker.'
+FROM style_directions sd WHERE sd.name ILIKE 'Shotokan'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'style_direction', sd.id, 'hints_on_progression',
+    'Nuancen in Stellung und Hüfttechnik, kein neuer Planungstyp.'
+FROM style_directions sd WHERE sd.name ILIKE 'Shotokan'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'training_type', tt.id, 'description',
+    'Wettkampforientiertes Training mit höherer Anspruchskurve und belastungsnahen Phasen.'
+FROM training_types tt WHERE tt.name ILIKE 'Wettkampf'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'training_type', tt.id, 'hints_on_path_qa',
+    'Spezialisierung, Kombination und Belastung unter Druck sind relevant; Lücken in Anwendungs- oder Perfektionsphasen können echte Hinweise sein.'
+FROM training_types tt WHERE tt.name ILIKE 'Wettkampf'
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();

backend/migrations/093_ai_prompt_catalog_granular_placeholders.sql

@@ -0,0 +1,199 @@
+-- Migration 093: Planungs-KI — granulare Katalog-Slot-Platzhalter in Prompt-Templates
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und prüfst einen vorgeschlagenen Übungspfad.
+
+Ziel-Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+Schritte (JSON): {{steps_json}}
+Erkannte Lücken: {{gaps_json}}
+Eingefügte Brücken: {{bridge_inserts_json}}
+
+Katalog-Kontext für Bewertung (Trainer-Auswahl — leere Zeilen ignorieren):
+
+Primärfokus:
+{{focus_area_description}}
+QS: {{focus_area_hints_on_path_qa}}
+Vermeiden: {{focus_area_anti_patterns}}
+
+Trainingsstil:
+{{training_type_description}}
+QS: {{training_type_hints_on_path_qa}}
+
+Zielgruppe:
+{{target_group_description}}
+QS: {{target_group_hints_on_path_qa}}
+
+Stilrichtung:
+{{style_direction_description}}
+QS: {{style_direction_hints_on_path_qa}}
+
+{{catalog_context_json}}
+
+Wichtig: Wenn Katalog-Slots gesetzt sind, haben diese Vorrang vor allgemeinen Technik-/Wettkampf-Maßstäben.
+
+Prüfe:
+1. Deckt der Pfad das Hauptthema der Anfrage ab (nicht nur Oberbegriffe)?
+2. Ist die Reihenfolge didaktisch sinnvoll (Einstieg → Vertiefung → Ziel)?
+3. Sind Sprünge zwischen benachbarten Schritten zu groß?
+4. Sind Brücken-Übungen sinnvoll oder überflüssig?
+5. Fehlen wichtige Zwischenschritte — gemäß Katalog-QS-Hinweisen, nicht pauschal „Perfektion“?
+6. Gibt es Schritte ohne Bezug zum Hauptthema?
+
+Wenn die Reihenfolge verbessert werden sollte: ordered_step_indices = Permutation der aktuellen 0-basierten Schritt-Indizes.
+Wenn wichtige Zwischenschritte fehlen: suggested_new_exercises mit Titel + Kurzskizze und insert_after_step_index.
+
+Antworte NUR mit JSON:
+{
+  "overall_ok": true,
+  "quality_score": 0.85,
+  "topic_coverage": "Kurz: wie gut das Hauptthema abgedeckt ist",
+  "ordered_step_indices": [0, 1, 2, 3],
+  "issues": ["…"],
+  "sequence_notes": ["…"],
+  "recommendations": ["…"],
+  "suggested_new_exercises": []
+}$t$,
+    default_template = template
+WHERE slug = 'planning_exercise_path_qa';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und analysierst eine Anfrage für einen Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+
+Katalog-Kontext (Primärfokus / Trainingsstil / Zielgruppe / Stil — leere Zeilen ignorieren):
+
+Primärfokus: {{focus_area_description}}
+Progression: {{focus_area_hints_on_progression}}
+
+Trainingsstil: {{training_type_description}}
+Progression: {{training_type_hints_on_progression}}
+
+Zielgruppe: {{target_group_description}}
+
+Stilrichtung: {{style_direction_description}}
+
+Wichtig: Keine Gruppenanalyse — nur didaktischer Pfad. Katalog-Hinweise beachten.
+
+Antworte NUR mit JSON:
+{
+  "primary_topic": "Mae Geri",
+  "start_assumption": "Welche Voraussetzungen werden für den Einstieg angenommen",
+  "target_state": "Konkreter Zielzustand der Progression",
+  "success_criteria": ["messbare Kriterien"],
+  "constraints": { "partner_required": false }
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_goal_analysis';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und erstellst eine didaktische Roadmap für einen Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Zielanalyse: {{goal_analysis_json}}
+Semantic Brief: {{semantic_brief_json}}
+Anzahl Major Steps (N): {{max_steps}}
+
+Katalog-Kontext für Stufenlogik:
+
+Primärfokus:
+{{focus_area_description}}
+Roadmap: {{focus_area_hints_on_progression}}
+Vermeiden: {{focus_area_anti_patterns}}
+
+Trainingsstil:
+{{training_type_description}}
+Roadmap: {{training_type_hints_on_progression}}
+
+Zielgruppe:
+{{target_group_description}}
+Roadmap: {{target_group_hints_on_progression}}
+
+Stilrichtung:
+{{style_direction_description}}
+Roadmap: {{style_direction_hints_on_progression}}
+
+{{catalog_context_json}}
+
+Erzeuge zuerst 8–12 micro_objectives, dann konsolidiere auf genau N major_steps.
+Phasen: einstieg, grundlage, vertiefung, anwendung, perfektion — Katalog-Roadmap-Hinweise beachten.
+
+Antworte NUR mit JSON:
+{
+  "micro_objectives": [
+    { "id": "m1", "phase": "grundlage", "title": "…", "weight": 0.9, "depends_on": [] }
+  ],
+  "major_steps": [
+    { "index": 0, "phase": "grundlage", "learning_goal": "…", "consolidates": ["m1","m2"], "rationale": "…" }
+  ],
+  "consolidation_notes": ["…"]
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_roadmap';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und spezifizierst didaktische Stufen eines Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Zielanalyse: {{goal_analysis_json}}
+Major Steps: {{major_steps_json}}
+Intent-Kontext: {{intent_context_json}}
+Semantic Brief: {{semantic_brief_json}}
+
+Katalog-Kontext je Stufe:
+
+Primärfokus — Progression: {{focus_area_hints_on_progression}}
+Primärfokus — Vermeiden: {{focus_area_anti_patterns}}
+
+Trainingsstil — Progression: {{training_type_hints_on_progression}}
+Trainingsstil — Vermeiden: {{training_type_anti_patterns}}
+
+Zielgruppe — Progression: {{target_group_hints_on_progression}}
+Zielgruppe — Vermeiden: {{target_group_anti_patterns}}
+
+Stilrichtung — Progression: {{style_direction_hints_on_progression}}
+
+{{catalog_context_json}}
+
+Für jeden Major Step: messbares Lernziel, load_profile, exercise_type, success_criteria, anti_patterns — Katalog-Slots beachten.
+
+Antworte NUR mit JSON:
+{
+  "stage_specs": [
+    {
+      "major_step_index": 0,
+      "learning_goal": "…",
+      "load_profile": ["koordination", "gleichgewicht"],
+      "exercise_type": "kihon_einzel",
+      "success_criteria": ["…"],
+      "anti_patterns": ["…"]
+    }
+  ]
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_stage_spec';
+
+UPDATE ai_prompts
+SET template = $t$Du bist Assistent für Kampfsport-Trainer und extrahierst Start, Ziel und Ergänzungen für einen Progressionsgraphen.
+
+Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+Trainer-Notizen: {{user_notes}}
+
+Katalog-Einordnung:
+Primärfokus: {{focus_area_description}}
+Trainingsstil: {{training_type_description}}
+Zielgruppe: {{target_group_description}}
+
+Antworte NUR mit JSON:
+{
+  "primary_topic": "…",
+  "start_situation": "…",
+  "target_state": "…",
+  "roadmap_notes": "…",
+  "extraction_notes": "…"
+}$t$,
+    default_template = template
+WHERE slug = 'planning_progression_start_target';

backend/migrations/094_catalog_prompt_slots_full_seed.sql

@@ -0,0 +1,167 @@
+-- Migration 094: Vollständige Befüllung catalog_prompt_slots (H1-Inhalte + Defaults für alle Stammdaten)
+
+CREATE TEMP TABLE IF NOT EXISTS _catalog_slot_seed (
+    catalog_kind VARCHAR(32) NOT NULL,
+    name_pattern TEXT NOT NULL,
+    slot_key VARCHAR(64) NOT NULL,
+    content TEXT NOT NULL
+);
+
+TRUNCATE _catalog_slot_seed;
+
+-- Primärfokus Karate (häufigster Technik-Pfad)
+INSERT INTO _catalog_slot_seed (catalog_kind, name_pattern, slot_key, content) VALUES
+('focus_area', 'Karate', 'description',
+ 'Technik-Curriculum im Karate-Kontext: aufeinander aufbauende Kihon-Progression mit klaren Qualitätsankern (Stand, Hüfte, Kime).'),
+('focus_area', 'Karate', 'hints_on_progression',
+ 'Typische Phasen: Einstieg → Grundlagen → Koordination/Kraft → Anwendung → optional Vertiefung; Grundlagen vor Perfektion.'),
+('focus_area', 'Karate', 'hints_on_exercise',
+ 'Kihon und Partnerübungen mit Technikbezug; reine Kraft-/Ausdauer-Inseln nur mit klarer Begründung.'),
+('focus_area', 'Karate', 'hints_on_path_qa',
+ 'Kohärente Progression Grundlagen → Anwendung → Vertiefung; Übergänge ohne Sprünge; themenfremde Kraft-/Ausdauer-Inseln abwerten.'),
+('focus_area', 'Karate', 'anti_patterns',
+ 'Keine pauschale Perfektions-Stufe verlangen, wenn der Trainingsstil Breitensport ist.');
+
+-- Selbstverteidigung
+INSERT INTO _catalog_slot_seed VALUES
+('focus_area', 'Selbstverteidigung', 'description',
+ 'Praktische Selbstverteidigung: realistische Szenarien, Sicherheit und anwendungsnahe Progression — nicht Show-Technik oder Wettkampf-Kata.'),
+('focus_area', 'Selbstverteidigung', 'hints_on_progression',
+ 'Von Wahrnehmung und Distanz zu einfachen Abwehrmustern und kontrollierter Anwendung.'),
+('focus_area', 'Selbstverteidigung', 'hints_on_exercise',
+ 'Partnerübungen mit klaren Sicherheitsregeln; Szenario-Bezug wichtiger als Stil-Show.'),
+('focus_area', 'Selbstverteidigung', 'hints_on_path_qa',
+ 'Lücken bei Szenario- oder Sicherheitsstufen sind relevant; fehlende Kick-Varianten oder Wettkampftiefe sind kein Mangel.'),
+('focus_area', 'Selbstverteidigung', 'anti_patterns',
+ 'Keine Wettkampf- oder Kata-Perfektion als QS-Maßstab.');
+
+-- Gewaltschutz (ergänzt 092)
+INSERT INTO _catalog_slot_seed VALUES
+('focus_area', 'Gewaltschutz', 'hints_on_progression',
+ 'Phasen: Wahrnehmung → Grenzen → Deeskalation → sichere Übungsformen; keine Kumite-Perfektionsstufen erzwingen.'),
+('focus_area', 'Gewaltschutz', 'hints_on_exercise',
+ 'Übungen mit Rollen, Kommunikation, Ausweichen; keine rein technischen Kick-Fokus-Inseln ohne Bezug.');
+
+-- Fitness (falls vorhanden)
+INSERT INTO _catalog_slot_seed VALUES
+('focus_area', 'Fitness', 'description',
+ 'Fitness- und Konditionsorientierung mit sicherer Belastungssteuerung; Technikbezug nur wo fachlich sinnvoll.'),
+('focus_area', 'Fitness', 'hints_on_progression',
+ 'Progression von niedriger zu moderater Belastung; klare Pausen und Technikhygiene.'),
+('focus_area', 'Fitness', 'hints_on_path_qa',
+ 'Keine Wettkampf-Spezialisierung als Pflicht-Kriterium; Belastungssteigerung ohne Technikbezug abwerten.'),
+('focus_area', 'Fitness', 'anti_patterns',
+ 'Keine Kumite-Perfektion oder Wettkampf-Kombinationen als QS-Maßstab verlangen.');
+
+-- Trainingsstile (global)
+INSERT INTO _catalog_slot_seed VALUES
+('training_type', 'Breitensport', 'hints_on_progression',
+ 'Moderater Schwierigkeitsanstieg; Perfektionsphasen optional.'),
+('training_type', 'Breitensport', 'anti_patterns',
+ 'Keine Leistungssport-Perfektion als Pflicht-Lücke.'),
+('training_type', 'Leistungssport', 'description',
+ 'Leistungsorientiertes Training mit höherer Anspruchskurve und Spezialisierung.'),
+('training_type', 'Leistungssport', 'hints_on_progression',
+ 'Belastungs- und Kombinationsprogressionen sind erwünscht.'),
+('training_type', 'Leistungssport', 'hints_on_path_qa',
+ 'Höhere Anspruchskurven sind passend; Lücken in Spezialisierung können echte Hinweise sein.'),
+('training_type', 'Wettkampf', 'hints_on_progression',
+ 'Anwendungs- und Druckphasen zeitig einplanen.');
+
+-- Zielgruppen
+INSERT INTO _catalog_slot_seed VALUES
+('target_group', 'Breitensportler', 'description',
+ 'Breitensport: Partizipation und Verständlichkeit vor maximaler Spezialisierung.'),
+('target_group', 'Breitensportler', 'hints_on_path_qa',
+ 'Moderate Progression; Perfektions-Lücken sind selten echte Mängel.'),
+('target_group', 'Breitensportler', 'anti_patterns',
+ 'Keine Leistungssport-Perfektion als Pflicht-Kriterium.'),
+('target_group', 'Kinder', 'hints_on_progression',
+ 'Spielerische Einstiege; kurze Abschnitte; Sicherheit vor Perfektion.'),
+('target_group', 'Leistungssportler', 'hints_on_progression',
+ 'Anspruchskurve und Spezialisierung dürfen steiler sein.');
+
+-- Stilrichtungen (generisch + Shotokan-Details via 092)
+INSERT INTO _catalog_slot_seed VALUES
+('style_direction', 'Goju-Ryu', 'hints_on_progression',
+ 'Stil-Nuancen (Stand, Atem, Kime) einbeziehen — kein Stilwechsel erzwingen.'),
+('style_direction', 'Wado-Ryu', 'hints_on_progression',
+ 'Stil-Nuancen (Stand, Atem, Kime) einbeziehen — kein Stilwechsel erzwingen.'),
+('style_direction', 'Shito-Ryu', 'hints_on_progression',
+ 'Stil-Nuancen (Stand, Atem, Kime) einbeziehen — kein Stilwechsel erzwingen.'),
+('style_direction', 'Kyokushin', 'hints_on_progression',
+ 'Stil-Nuancen (Stand, Belastung, Kime) einbeziehen — kein Stilwechsel erzwingen.');
+
+-- Fokusbereiche: aus Seed-Tabelle
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT s.catalog_kind, fa.id, s.slot_key, s.content
+FROM _catalog_slot_seed s
+JOIN focus_areas fa ON fa.name ILIKE s.name_pattern
+WHERE s.catalog_kind = 'focus_area'
+ON CONFLICT (catalog_kind, catalog_id, slot_key)
+DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT s.catalog_kind, tt.id, s.slot_key, s.content
+FROM _catalog_slot_seed s
+JOIN training_types tt ON tt.name ILIKE s.name_pattern
+WHERE s.catalog_kind = 'training_type'
+ON CONFLICT (catalog_kind, catalog_id, slot_key)
+DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT s.catalog_kind, tg.id, s.slot_key, s.content
+FROM _catalog_slot_seed s
+JOIN target_groups tg ON tg.name ILIKE s.name_pattern
+WHERE s.catalog_kind = 'target_group'
+ON CONFLICT (catalog_kind, catalog_id, slot_key)
+DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT s.catalog_kind, sd.id, s.slot_key, s.content
+FROM _catalog_slot_seed s
+JOIN style_directions sd ON sd.name ILIKE s.name_pattern
+WHERE s.catalog_kind = 'style_direction'
+ON CONFLICT (catalog_kind, catalog_id, slot_key)
+DO UPDATE SET content = EXCLUDED.content, updated_at = NOW();
+
+-- Default-Technik-Pack für Fokusbereiche ohne hints_on_path_qa (außer Gewaltschutz/Fitness)
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'focus_area', fa.id, 'hints_on_path_qa',
+ 'Kohärente Progression zum Anfrage-Thema; Lücken sind fehlende Zwischenstufen im Lernpfad, nicht fehlende Nebenthemen.'
+FROM focus_areas fa
+WHERE fa.name NOT ILIKE 'Gewaltschutz'
+  AND fa.name NOT ILIKE 'Fitness'
+  AND NOT EXISTS (
+    SELECT 1 FROM catalog_prompt_slots cps
+    WHERE cps.catalog_kind = 'focus_area' AND cps.catalog_id = fa.id AND cps.slot_key = 'hints_on_path_qa'
+      AND TRIM(cps.content) <> ''
+  )
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO NOTHING;
+
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'focus_area', fa.id, 'hints_on_progression',
+ 'Grundlagen vor Anwendung; moderate Sprünge zwischen Stufen vermeiden.'
+FROM focus_areas fa
+WHERE fa.name NOT ILIKE 'Gewaltschutz'
+  AND fa.name NOT ILIKE 'Fitness'
+  AND NOT EXISTS (
+    SELECT 1 FROM catalog_prompt_slots cps
+    WHERE cps.catalog_kind = 'focus_area' AND cps.catalog_id = fa.id AND cps.slot_key = 'hints_on_progression'
+      AND TRIM(cps.content) <> ''
+  )
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO NOTHING;
+
+-- Stilrichtungen ohne Eintrag: generischer Progressions-Hinweis
+INSERT INTO catalog_prompt_slots (catalog_kind, catalog_id, slot_key, content)
+SELECT 'style_direction', sd.id, 'hints_on_progression',
+ 'Stil-spezifische Nuancen (Stand, Hüfte, Rhythmus) einbeziehen — ohne Stilwechsel zu erzwingen.'
+FROM style_directions sd
+WHERE NOT EXISTS (
+    SELECT 1 FROM catalog_prompt_slots cps
+    WHERE cps.catalog_kind = 'style_direction' AND cps.catalog_id = sd.id AND cps.slot_key = 'hints_on_progression'
+      AND TRIM(cps.content) <> ''
+  )
+ON CONFLICT (catalog_kind, catalog_id, slot_key) DO NOTHING;
+
+DROP TABLE IF EXISTS _catalog_slot_seed;

backend/openrouter_chat.py

@@ -196,6 +196,13 @@ def openrouter_chat_completion(
             cc,
         )
 
+    try:
+        from planning_llm_usage import record_planning_llm_call
+
+        record_planning_llm_call(1)
+    except Exception:
+        pass
+
     return joined
 
 

backend/planning_catalog_prompt_snippets.py

@@ -0,0 +1,16 @@
+"""
+Katalog-Prompt-Snippets — Abwärtskompatibilität (H1-Importpfade).
+
+Implementierung: catalog_prompt_slots.py (H2).
+"""
+from catalog_prompt_slots import (
+    build_catalog_guidance_for_prompt,
+    get_rematch_guard_for_catalog,
+    pick_active_catalog_item,
+)
+
+__all__ = [
+    "build_catalog_guidance_for_prompt",
+    "get_rematch_guard_for_catalog",
+    "pick_active_catalog_item",
+]

backend/planning_exercise_path_builder.py

@@ -2082,6 +2082,7 @@ def _run_evaluate_only_path_qa(
     semantic_brief: PlanningSemanticBrief,
     steps: List[Dict[str, Any]],
     roadmap_ctx: Optional[ProgressionRoadmapContext],
+    catalog_context: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Dict[str, Any]:
     roadmap_first = roadmap_ctx is not None
     gaps: List[Dict[str, Any]] = []
@@ -2095,6 +2096,9 @@ def _run_evaluate_only_path_qa(
     gap_fill_offers: List[Dict[str, Any]] = []
     roadmap_qa_mode: Optional[str] = None
 
+    if catalog_context is None:
+        catalog_context = _resolve_planning_catalog_context(cur, body)
+
     if body.include_path_qa:
         if roadmap_first:
             roadmap_qa_mode = "roadmap_first_lite"
@@ -2115,6 +2119,7 @@ def _run_evaluate_only_path_qa(
                 steps=steps,
                 gaps=gaps,
                 bridge_inserts=bridge_inserts,
+                catalog=catalog_context,
             )
 
         off_topic_steps = detect_off_topic_steps(
@@ -2208,6 +2213,7 @@ def _run_evaluate_only_path_qa(
         reorder_notes=[],
         roadmap_qa_mode=roadmap_qa_mode,
         multistage_qa=multistage_qa,
+        steps=steps,
     )
     return {
         "path_qa": path_qa,
@@ -2500,6 +2506,7 @@ def _quick_evaluate_steps_qa(
         llm_applied=False,
         roadmap_qa_mode="roadmap_first_lite" if roadmap_first else None,
         multistage_qa=multistage_qa,
+        steps=steps_list,
     )
     if path_qa.get("quality_score") is None:
         path_qa["quality_score"] = compute_deterministic_path_quality_score(
@@ -3072,6 +3079,7 @@ def _suggestion_as_slot_diff(entry: Mapping[str, Any]) -> Dict[str, Any]:
 
 
 _SLOT_FIT_POOR_THRESHOLD = 0.30
+_SLOT_FIT_GOOD_THRESHOLD = 0.50
 
 
 def _off_topic_semantic_scores_by_slot(
@@ -3152,9 +3160,18 @@ def _slot_auto_select_library(
         return False
     if proposed_slot_score is None:
         return False
-    if baseline_slot_score is None:
+    effective_baseline = float(baseline_slot_score) if baseline_slot_score is not None else 0.0
+    if float(proposed_slot_score) <= effective_baseline + 0.001:
+        return False
+    # Leerer Slot: Bibliothek nur vorauswählen, wenn Stufen-Fit klar ausreicht.
+    if baseline_exercise_id is None:
+        return float(proposed_slot_score) >= _SLOT_FIT_GOOD_THRESHOLD
     return True
-    return float(proposed_slot_score) > float(baseline_slot_score) + 0.001
+
+
+def _slot_auto_select_ai(*, library_auto_select: bool, has_ai: bool) -> bool:
+    """KI-Vorschlag vorauswählen, wenn angeboten und Bibliothek nicht klar besser."""
+    return bool(has_ai and not library_auto_select)
 
 
 def _build_unified_slot_review_entry(
@@ -3391,10 +3408,14 @@ def _build_unified_slot_review_entry(
                 )
                 gap_fill_offers.append(slot_offer)
         if slot_offer:
+            ai_auto = _slot_auto_select_ai(
+                library_auto_select=bool(library_alt and library_alt.get("auto_select")),
+                has_ai=True,
+            )
             ai_alt = {
                 "title_hint": slot_offer.get("title_hint") or f"Slot {major_idx + 1}",
                 "gap_offer": slot_offer,
-                "auto_select": False,
+                "auto_select": ai_auto,
             }
 
     return {
@@ -3812,6 +3833,7 @@ def suggest_progression_path(
     roadmap_ctx: Optional[ProgressionRoadmapContext] = None
     roadmap_edited = False
     roadmap_structured = _roadmap_structured_from_body(body)
+    catalog_context = _resolve_planning_catalog_context(cur, body)
 
     if body.roadmap_override is not None:
         try:
@@ -3836,6 +3858,7 @@ def suggest_progression_path(
             cur=cur,
             include_llm_start_target=body.include_llm_start_target,
             structured=roadmap_structured,
+            catalog=catalog_context,
         )
         progression_roadmap = progression_roadmap_to_api_dict(roadmap_ctx)
     elif include_roadmap:
@@ -3847,6 +3870,7 @@ def suggest_progression_path(
             include_llm_roadmap=body.include_llm_roadmap,
             include_llm_start_target=body.include_llm_start_target,
             structured=roadmap_structured,
+            catalog=catalog_context,
         )
         progression_roadmap = progression_roadmap_to_api_dict(roadmap_ctx)
 
@@ -3907,6 +3931,7 @@ def suggest_progression_path(
             semantic_brief=semantic_brief,
             steps=eval_steps,
             roadmap_ctx=roadmap_ctx,
+            catalog_context=catalog_context,
         )
         return {
             "goal_query": goal_query,
@@ -3937,7 +3962,7 @@ def suggest_progression_path(
         start_situation=body.start_situation,
         target_state=body.target_state,
         roadmap_notes=body.roadmap_notes,
-        catalog_context=_resolve_planning_catalog_context(cur, body),
+        catalog_context=catalog_context,
     )
     path_skill_expectations: Optional[Dict[str, Any]] = None
     if roadmap_ctx and roadmap_ctx.goal_analysis:
@@ -4136,6 +4161,7 @@ def suggest_progression_path(
                 steps=steps,
                 gaps=gaps,
                 bridge_inserts=bridge_inserts,
+                catalog=catalog_context,
             )
 
         if (
@@ -4224,6 +4250,7 @@ def suggest_progression_path(
                 steps=steps,
                 gaps=gaps,
                 bridge_inserts=bridge_inserts,
+                catalog=catalog_context,
             )
 
         llm_gap_specs = parse_llm_suggested_new_exercises(
@@ -4312,6 +4339,7 @@ def suggest_progression_path(
         reorder_notes=reorder_notes,
         roadmap_qa_mode=roadmap_qa_mode,
         multistage_qa=multistage_qa,
+        steps=steps,
     )
     if rematch_log:
         path_qa["rematch_applied"] = True

backend/planning_exercise_path_qa.py

@@ -9,6 +9,8 @@ import re
 from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Set, Tuple
 
 from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
+from planning_catalog_context import ProgressionPlanningCatalogContext
+from planning_prompt_variables import merge_planning_prompt_variables
 from exercise_ai import strip_html_to_plain
 from openrouter_chat import (
     effective_openrouter_model_for_prompt_row,
@@ -320,6 +322,7 @@ def try_llm_qa_progression_path(
     steps: Sequence[Mapping[str, Any]],
     gaps: Sequence[Mapping[str, Any]],
     bridge_inserts: Sequence[Mapping[str, Any]],
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[Optional[Dict[str, Any]], bool]:
     api_key, _ = normalize_openrouter_env()
     if not api_key or len(steps) < 2:
@@ -354,13 +357,18 @@ def try_llm_qa_progression_path(
             }
         )
 
-    variables = {
+    variables = merge_planning_prompt_variables(
+        cur,
+        {
             "goal_query": goal_query or "",
             "semantic_brief_json": json.dumps(brief_to_summary_dict(brief), ensure_ascii=False),
             "steps_json": json.dumps(step_payload, ensure_ascii=False),
             "gaps_json": json.dumps(list(gaps), ensure_ascii=False),
             "bridge_inserts_json": json.dumps(list(bridge_inserts), ensure_ascii=False),
-    }
+        },
+        catalog=catalog,
+        slug="planning_exercise_path_qa",
+    )
 
     try:
         prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_path_qa", variables)
@@ -688,6 +696,160 @@ def find_step_pair_index(
     return None
 
 
+def count_step_assignment_stats(steps: Optional[Sequence[Mapping[str, Any]]]) -> Dict[str, int]:
+    stats = {"total": 0, "empty": 0, "library_filled": 0, "ai_proposal": 0}
+    for raw in steps or []:
+        if not isinstance(raw, dict):
+            continue
+        stats["total"] += 1
+        if raw.get("exercise_id") is not None and not raw.get("is_ai_proposal"):
+            stats["library_filled"] += 1
+        elif raw.get("is_ai_proposal"):
+            stats["ai_proposal"] += 1
+        else:
+            stats["empty"] += 1
+    return stats
+
+
+def compute_assignment_quality_score(
+    *,
+    steps: Optional[Sequence[Mapping[str, Any]]] = None,
+    off_topic_steps: Optional[Sequence[Mapping[str, Any]]] = None,
+    gaps: Optional[Sequence[Mapping[str, Any]]] = None,
+) -> float:
+    """QS der Übungsbesetzung — leere Slots stark abwerten."""
+    stats = count_step_assignment_stats(steps)
+    total = stats["total"]
+    if total <= 0:
+        return 0.45
+    empty = stats["empty"]
+    library = stats["library_filled"]
+    ai = stats["ai_proposal"]
+    fill_credit = (library + 0.55 * ai) / total
+    score = 0.1 + 0.84 * fill_credit
+    if empty > 0:
+        score -= 0.22 * (empty / total)
+    score -= 0.08 * len(off_topic_steps or [])
+    score -= 0.03 * len(gaps or [])
+    return max(0.08, min(0.98, round(score, 4)))
+
+
+def compute_roadmap_quality_score(
+    *,
+    llm_qa: Optional[Mapping[str, Any]] = None,
+    llm_applied: bool = False,
+    gaps: Optional[Sequence[Mapping[str, Any]]] = None,
+    multistage_qa: Optional[Mapping[str, Any]] = None,
+) -> float:
+    """QS der Roadmap-/Stufenlogik — unabhängig von Slot-Befüllung."""
+    if llm_applied and llm_qa and llm_qa.get("quality_score") is not None:
+        try:
+            return max(0.08, min(0.98, round(float(llm_qa["quality_score"]), 4)))
+        except (TypeError, ValueError):
+            pass
+    score = 0.9
+    score -= 0.05 * len(gaps or [])
+    hint_count = int((multistage_qa or {}).get("optimization_hint_count") or 0)
+    score -= min(0.12, 0.015 * hint_count)
+    return max(0.35, min(0.98, round(score, 4)))
+
+
+def build_assignment_qa_snapshot(
+    *,
+    steps: Optional[Sequence[Mapping[str, Any]]] = None,
+    off_topic_steps: Optional[Sequence[Mapping[str, Any]]] = None,
+    gaps: Optional[Sequence[Mapping[str, Any]]] = None,
+) -> Dict[str, Any]:
+    off_topic = list(off_topic_steps or [])
+    stats = count_step_assignment_stats(steps)
+    score = compute_assignment_quality_score(
+        steps=steps,
+        off_topic_steps=off_topic,
+        gaps=gaps,
+    )
+    issues: List[str] = []
+    if stats["empty"] > 0:
+        issues.append(
+            f"{stats['empty']} von {stats['total']} Slot(s) ohne Übung — bitte Bibliothek oder KI-Vorschlag zuweisen",
+        )
+    if stats["ai_proposal"] > 0 and stats["library_filled"] == 0 and stats["empty"] > 0:
+        issues.append(
+            f"{stats['ai_proposal']} KI-Entwurf(e), aber noch {stats['empty']} leere Slot(s)",
+        )
+    for item in off_topic[:5]:
+        title = (item.get("title") or "Schritt").strip()
+        issues.append(f"„{title}“ passt nicht zum Stufen-Ziel")
+    overall_ok = stats["empty"] == 0 and len(off_topic) == 0
+    return {
+        "overall_ok": overall_ok,
+        "quality_score": score,
+        "slot_count": stats["total"],
+        "empty_slot_count": stats["empty"],
+        "library_filled_count": stats["library_filled"],
+        "ai_proposal_count": stats["ai_proposal"],
+        "issues": issues,
+    }
+
+
+def build_roadmap_qa_snapshot(
+    *,
+    llm_qa: Optional[Mapping[str, Any]] = None,
+    llm_applied: bool = False,
+    gaps: Optional[Sequence[Mapping[str, Any]]] = None,
+    multistage_qa: Optional[Mapping[str, Any]] = None,
+    roadmap_qa_mode: Optional[str] = None,
+) -> Dict[str, Any]:
+    score = compute_roadmap_quality_score(
+        llm_qa=llm_qa,
+        llm_applied=llm_applied,
+        gaps=gaps,
+        multistage_qa=multistage_qa,
+    )
+    issues: List[str] = []
+    if not llm_applied:
+        for gap in gaps or []:
+            issues.append(
+                f"Übergang „{gap.get('from_title')}“ → „{gap.get('to_title')}“ schwach (Score {gap.get('gap_score')})",
+            )
+    if llm_applied and llm_qa:
+        issues.extend(str(x).strip() for x in (llm_qa.get("issues") or []) if str(x).strip())
+    overall_ok = bool(llm_qa.get("overall_ok", True)) if llm_applied and llm_qa else len(gaps or []) == 0
+    snapshot: Dict[str, Any] = {
+        "overall_ok": overall_ok,
+        "quality_score": score,
+        "issues": issues[:8],
+        "llm_applied": bool(llm_applied),
+        "roadmap_qa_mode": roadmap_qa_mode,
+    }
+    if llm_applied and llm_qa:
+        snapshot["topic_coverage"] = llm_qa.get("topic_coverage")
+        snapshot["recommendations"] = list(llm_qa.get("recommendations") or [])
+        snapshot["sequence_notes"] = list(llm_qa.get("sequence_notes") or [])
+    return snapshot
+
+
+def merge_path_quality_scores(
+    roadmap_qa: Mapping[str, Any],
+    assignment_qa: Mapping[str, Any],
+) -> float:
+    """Gesamt-QS: schwächere Dimension begrenzt — leere Slots senken den Pfad deutlich."""
+    try:
+        roadmap_score = float(roadmap_qa.get("quality_score"))
+    except (TypeError, ValueError):
+        roadmap_score = None
+    try:
+        assignment_score = float(assignment_qa.get("quality_score"))
+    except (TypeError, ValueError):
+        assignment_score = None
+    if roadmap_score is not None and assignment_score is not None:
+        return round(min(roadmap_score, assignment_score), 4)
+    if assignment_score is not None:
+        return assignment_score
+    if roadmap_score is not None:
+        return roadmap_score
+    return 0.5
+
+
 def build_path_qa_summary(
     *,
     gaps: Sequence[Mapping[str, Any]],
@@ -702,6 +864,7 @@ def build_path_qa_summary(
     reorder_notes: Optional[Sequence[str]] = None,
     roadmap_qa_mode: Optional[str] = None,
     multistage_qa: Optional[Mapping[str, Any]] = None,
+    steps: Optional[Sequence[Mapping[str, Any]]] = None,
 ) -> Dict[str, Any]:
     offers = list(gap_fill_offers or [])
     off_topic = list(off_topic_steps or [])
@@ -726,31 +889,32 @@ def build_path_qa_summary(
         summary["qa_tiers"] = list(multistage_qa.get("qa_tiers") or [])
         summary["optimization_hints"] = list(multistage_qa.get("optimization_hints") or [])
         summary["optimization_hint_count"] = int(multistage_qa.get("optimization_hint_count") or 0)
-    if llm_qa:
-        summary["overall_ok"] = bool(llm_qa.get("overall_ok", True))
-        summary["quality_score"] = llm_qa.get("quality_score")
-        summary["issues"] = list(llm_qa.get("issues") or [])
-        summary["sequence_notes"] = list(llm_qa.get("sequence_notes") or [])
-        summary["topic_coverage"] = llm_qa.get("topic_coverage")
-        summary["recommendations"] = list(llm_qa.get("recommendations") or [])
-        summary["suggested_new_exercises"] = list(llm_qa.get("suggested_new_exercises") or [])
-    else:
-        summary["overall_ok"] = len(gaps) == 0 and len(off_topic) == 0
-        summary["issues"] = [
-            f"Lücke zwischen „{g.get('from_title')}“ und „{g.get('to_title')}“ (Score {g.get('gap_score')})"
-            for g in gaps
-        ] if gaps else []
-        if off_topic:
-            summary["issues"] = list(summary["issues"]) + [
-                f"Schritt „{o.get('title')}“ passt nicht zum Pfad-Thema"
-                for o in off_topic
-            ]
-        summary["quality_score"] = compute_deterministic_path_quality_score(
-            gaps=gaps,
-            off_topic_steps=off_topic,
+
+    assignment_qa = build_assignment_qa_snapshot(
         steps=steps,
-            multistage_qa=multistage_qa,
+        off_topic_steps=off_topic,
+        gaps=gaps,
     )
+    roadmap_qa = build_roadmap_qa_snapshot(
+        llm_qa=llm_qa,
+        llm_applied=llm_applied,
+        gaps=gaps,
+        multistage_qa=multistage_qa,
+        roadmap_qa_mode=roadmap_qa_mode,
+    )
+    summary["assignment_qa"] = assignment_qa
+    summary["roadmap_qa"] = roadmap_qa
+    summary["quality_score"] = merge_path_quality_scores(roadmap_qa, assignment_qa)
+    summary["overall_ok"] = bool(
+        assignment_qa.get("overall_ok")
+        and roadmap_qa.get("overall_ok", True),
+    )
+    summary["topic_coverage"] = roadmap_qa.get("topic_coverage")
+    summary["recommendations"] = list(roadmap_qa.get("recommendations") or [])
+    summary["sequence_notes"] = list(roadmap_qa.get("sequence_notes") or [])
+    summary["issues"] = list(assignment_qa.get("issues") or []) + list(roadmap_qa.get("issues") or [])[:6]
+    if llm_qa:
+        summary["suggested_new_exercises"] = list(llm_qa.get("suggested_new_exercises") or [])
     return summary
 
 
@@ -761,31 +925,34 @@ def compute_deterministic_path_quality_score(
     steps: Optional[Sequence[Mapping[str, Any]]] = None,
     multistage_qa: Optional[Mapping[str, Any]] = None,
 ) -> float:
-    """Heuristische Pfad-QS ohne LLM — Basis für Slot-Vergleiche."""
-    score = 0.92
-    score -= 0.08 * len(off_topic_steps or [])
-    score -= 0.05 * len(gaps or [])
-    if steps:
-        empty = sum(
-            1
-            for s in steps
-            if isinstance(s, dict)
-            and s.get("exercise_id") is None
-            and not s.get("is_ai_proposal")
+    """Heuristische Pfad-QS ohne LLM — Roadmap + Besetzung kombiniert."""
+    roadmap_qa = build_roadmap_qa_snapshot(
+        llm_qa=None,
+        llm_applied=False,
+        gaps=gaps,
+        multistage_qa=multistage_qa,
     )
-        score -= 0.06 * empty
-    hint_count = int((multistage_qa or {}).get("optimization_hint_count") or 0)
-    score -= min(0.14, 0.02 * hint_count)
-    return max(0.35, min(0.98, round(score, 4)))
+    assignment_qa = build_assignment_qa_snapshot(
+        steps=steps,
+        off_topic_steps=off_topic_steps,
+        gaps=gaps,
+    )
+    return merge_path_quality_scores(roadmap_qa, assignment_qa)
 
 
 __all__ = [
     "apply_llm_path_reorder",
+    "build_assignment_qa_snapshot",
     "build_path_qa_summary",
+    "build_roadmap_qa_snapshot",
+    "compute_assignment_quality_score",
     "compute_deterministic_path_quality_score",
+    "compute_roadmap_quality_score",
+    "count_step_assignment_stats",
     "detect_off_topic_steps",
     "detect_path_gaps",
     "is_roadmap_planned_neighbor_pair",
+    "merge_path_quality_scores",
     "strip_off_topic_steps_from_path",
     "find_step_pair_index",
     "insert_bridge_exercises",

backend/planning_llm_usage.py

@@ -0,0 +1,62 @@
+"""
+Zähler für produktive OpenRouter-Aufrufe innerhalb einer Planungs-API-Anfrage.
+
+Wird per ContextVar gesetzt (Router: ``planning_llm_call_meter``); ``openrouter_chat_completion``
+erhöht den Zähler nach erfolgreicher Antwort — nur wenn ein Meter aktiv ist.
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Iterator, Optional
+
+_llm_call_counter: ContextVar[Optional["PlanningLlmCallCounter"]] = ContextVar(
+    "planning_llm_call_counter",
+    default=None,
+)
+
+
+class PlanningLlmCallCounter:
+    """Anzahl erfolgreicher OpenRouter-Chat-Completions in einem Request-Kontext."""
+
+    __slots__ = ("count",)
+
+    def __init__(self) -> None:
+        self.count = 0
+
+    def record(self, amount: int = 1) -> None:
+        try:
+            n = int(amount)
+        except (TypeError, ValueError):
+            n = 1
+        if n > 0:
+            self.count += n
+
+
+def current_planning_llm_call_counter() -> Optional[PlanningLlmCallCounter]:
+    return _llm_call_counter.get()
+
+
+def record_planning_llm_call(amount: int = 1) -> None:
+    counter = _llm_call_counter.get()
+    if counter is not None:
+        counter.record(amount)
+
+
+@contextmanager
+def planning_llm_call_meter() -> Iterator[PlanningLlmCallCounter]:
+    """Aktiviert LLM-Zählung für den umschlossenen Block (inkl. verschachtelter Aufrufe)."""
+    counter = PlanningLlmCallCounter()
+    token = _llm_call_counter.set(counter)
+    try:
+        yield counter
+    finally:
+        _llm_call_counter.reset(token)
+
+
+__all__ = [
+    "PlanningLlmCallCounter",
+    "current_planning_llm_call_counter",
+    "planning_llm_call_meter",
+    "record_planning_llm_call",
+]

backend/planning_progression_roadmap.py

@@ -18,6 +18,8 @@ from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple
 from pydantic import BaseModel, Field, ValidationError
 
 from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
+from planning_catalog_context import ProgressionPlanningCatalogContext
+from planning_prompt_variables import merge_planning_prompt_variables
 from openrouter_chat import (
     effective_openrouter_model_for_prompt_row,
     normalize_openrouter_env,
@@ -190,12 +192,20 @@ def _run_prompt_json(
     cur,
     slug: str,
     variables: Dict[str, str],
+    *,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Optional[Dict[str, Any]]:
     api_key, _ = normalize_openrouter_env()
     if not api_key or cur is None:
         return None
+    merged = merge_planning_prompt_variables(
+        cur,
+        variables,
+        catalog=catalog,
+        slug=slug,
+    )
     try:
-        prow, rendered = load_and_render_ai_prompt(cur, slug, variables)
+        prow, rendered = load_and_render_ai_prompt(cur, slug, merged)
         model = effective_openrouter_model_for_prompt_row(prow)
         raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
         return _extract_json_object(raw)
@@ -212,6 +222,7 @@ def try_llm_start_target_extract(
     goal_query: str,
     brief: PlanningSemanticBrief,
     user_notes: str = "",
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[Optional[StartTargetExtractArtifact], bool]:
     obj = _run_prompt_json(
         cur,
@@ -221,6 +232,7 @@ def try_llm_start_target_extract(
             "semantic_brief_json": json.dumps(brief_to_summary_dict(brief), ensure_ascii=False),
             "user_notes": (user_notes or "").strip(),
         },
+        catalog=catalog,
     )
     if not obj:
         return None, False
@@ -236,6 +248,7 @@ def try_llm_goal_analysis(
     *,
     goal_query: str,
     brief: PlanningSemanticBrief,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[Optional[GoalAnalysisArtifact], bool]:
     obj = _run_prompt_json(
         cur,
@@ -244,6 +257,7 @@ def try_llm_goal_analysis(
             "goal_query": goal_query or "",
             "semantic_brief_json": json.dumps(brief_to_summary_dict(brief), ensure_ascii=False),
         },
+        catalog=catalog,
     )
     if not obj:
         return None, False
@@ -261,6 +275,7 @@ def try_llm_roadmap(
     brief: PlanningSemanticBrief,
     goal_analysis: GoalAnalysisArtifact,
     max_steps: int,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[Optional[RoadmapArtifact], bool]:
     obj = _run_prompt_json(
         cur,
@@ -271,6 +286,7 @@ def try_llm_roadmap(
             "goal_analysis_json": json.dumps(goal_analysis.model_dump(), ensure_ascii=False),
             "max_steps": str(int(max_steps)),
         },
+        catalog=catalog,
     )
     if not obj:
         return None, False
@@ -304,6 +320,7 @@ def try_llm_stage_specs(
     major_steps: Sequence[MajorStep],
     intent_context: Optional[Mapping[str, Any]] = None,
     semantic_brief: Optional[PlanningSemanticBrief] = None,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[Optional[List[StageSpecArtifact]], bool]:
     obj = _run_prompt_json(
         cur,
@@ -318,6 +335,7 @@ def try_llm_stage_specs(
                 ensure_ascii=False,
             ),
         },
+        catalog=catalog,
     )
     if not obj:
         return None, False
@@ -380,6 +398,7 @@ def resolve_roadmap_structured_input(
     brief: PlanningSemanticBrief,
     cur=None,
     include_llm: bool = False,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[RoadmapStructuredInput, StartTargetResolveMeta, Optional[StartTargetExtractArtifact]]:
     """Priorität je Feld: Trainer-Eingabe > LLM-Extraktion > Regex (von … bis …)."""
     user = structured or RoadmapStructuredInput()
@@ -395,6 +414,7 @@ def resolve_roadmap_structured_input(
             goal_query=goal_query,
             brief=brief,
             user_notes=user_notes,
+            catalog=catalog,
         )
 
     parsed_start, parsed_target = parse_start_target_from_goal_query(goal_query)
@@ -1068,6 +1088,7 @@ def run_start_target_resolve_only(
     cur=None,
     include_llm_start_target: bool = True,
     structured: Optional[RoadmapStructuredInput] = None,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> ProgressionRoadmapContext:
     """Nur Start/Ziel/Ergänzungen auflösen — ohne Roadmap-Stufen (Review vor Major Steps)."""
     brief = semantic_brief or build_semantic_brief(goal_query)
@@ -1077,6 +1098,7 @@ def run_start_target_resolve_only(
         brief=brief,
         cur=cur,
         include_llm=include_llm_start_target,
+        catalog=catalog,
     )
     topic_override = None
     if llm_extract and (llm_extract.primary_topic or "").strip():
@@ -1112,6 +1134,7 @@ def run_progression_roadmap_pipeline(
     include_llm_roadmap: bool = False,
     include_llm_start_target: bool = False,
     structured: Optional[RoadmapStructuredInput] = None,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> ProgressionRoadmapContext:
     """Workflow-lite: Phase A → B → C. LLM über ai_prompts-Slugs; deterministischer Fallback."""
     brief = semantic_brief or build_semantic_brief(goal_query)
@@ -1121,6 +1144,7 @@ def run_progression_roadmap_pipeline(
         brief=brief,
         cur=cur,
         include_llm=include_llm_start_target,
+        catalog=catalog,
     )
     parsed_start, parsed_target = parse_start_target_from_goal_query(goal_query)
     llm_goal_query = _roadmap_llm_goal_block(
@@ -1152,7 +1176,9 @@ def run_progression_roadmap_pipeline(
         topic_override=topic_override,
     )
     if include_llm_roadmap and cur is not None:
-        llm_ga, ga_ok = try_llm_goal_analysis(cur, goal_query=llm_goal_query, brief=brief)
+        llm_ga, ga_ok = try_llm_goal_analysis(
+            cur, goal_query=llm_goal_query, brief=brief, catalog=catalog
+        )
         if ga_ok and llm_ga:
             goal_analysis = _merge_structured_into_goal_analysis(
                 llm_ga,
@@ -1172,6 +1198,7 @@ def run_progression_roadmap_pipeline(
             brief=brief,
             goal_analysis=goal_analysis,
             max_steps=max_steps,
+            catalog=catalog,
         )
         if rm_ok and llm_rm:
             roadmap = llm_rm
@@ -1234,6 +1261,7 @@ def run_progression_roadmap_pipeline(
             major_steps=roadmap.major_steps,
             intent_context=intent.to_api_dict(),
             semantic_brief=brief,
+            catalog=catalog,
         )
         if spec_ok and llm_specs:
             stage_specs = list(llm_specs)

backend/planning_prompt_variables.py

@@ -0,0 +1,118 @@
+"""
+Zentrale Mustache-Variablen für Planungs-KI-Prompts.
+
+Orchestratoren bauen domänenspezifische Basis-Variablen; dieses Modul merged
+erweiterbare Provider (Katalog-Slots, später weitere Kontexte).
+"""
+from __future__ import annotations
+
+from typing import Any, Callable, Dict, Mapping, Optional
+
+from catalog_prompt_slots import all_placeholder_keys, empty_catalog_variables
+from planning_catalog_context import ProgressionPlanningCatalogContext
+
+PlanningPromptVariableProvider = Callable[..., Dict[str, str]]
+
+
+def _catalog_slot_variables(
+    *,
+    cur,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
+    slug: Optional[str] = None,
+    **_: Any,
+) -> Dict[str, str]:
+    if cur is None or catalog is None:
+        return empty_catalog_variables()
+    from catalog_prompt_slots import resolve_catalog_prompt_variables
+
+    resolved = resolve_catalog_prompt_variables(cur, catalog, slug=slug)
+    return {k: str(resolved.get(k) or "") for k in all_placeholder_keys()}
+
+
+_PLANNING_PROMPT_VARIABLE_PROVIDERS: tuple[PlanningPromptVariableProvider, ...] = (
+    _catalog_slot_variables,
+)
+
+
+def merge_planning_prompt_variables(
+    cur,
+    base_variables: Mapping[str, str],
+    *,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
+    slug: Optional[str] = None,
+) -> Dict[str, str]:
+    """Merged Basis-Variablen mit allen registrierten Planungs-Providern."""
+    out = {str(k): "" if v is None else str(v) for k, v in base_variables.items()}
+    ctx: Dict[str, Any] = {"cur": cur, "catalog": catalog, "slug": slug}
+    for provider in _PLANNING_PROMPT_VARIABLE_PROVIDERS:
+        out.update(provider(**ctx))
+    return out
+
+
+def planning_prompt_placeholder_catalog() -> dict:
+    """Platzhalter-Katalog für Admin — Slot-Typ × Dimension + Aggregat."""
+    from catalog_prompt_slots import CATALOG_KINDS, SLOT_KEYS, placeholder_key
+
+    slot_labels = {
+        "description": "Allgemeine Beschreibung",
+        "hints_on_progression": "Hinweise Progressionsgraph / Stufen",
+        "hints_on_exercise": "Hinweise Übungsanlage / Gap-Fill",
+        "hints_on_path_qa": "Bewertungsmaßstäbe Pfad-QS",
+        "anti_patterns": "Anti-Patterns (Fehlbewertung vermeiden)",
+        "rematch_guard": "Rematch-Guard (primär Code, optional Prompt)",
+    }
+    kind_labels = {c.kind: c.label_de for c in CATALOG_KINDS}
+
+    slugs_common = [
+        "planning_exercise_path_qa",
+        "planning_progression_roadmap",
+        "planning_progression_stage_spec",
+        "planning_progression_goal_analysis",
+        "planning_progression_start_target",
+    ]
+
+    defs = []
+    for cfg in CATALOG_KINDS:
+        for slot in SLOT_KEYS:
+            key = placeholder_key(cfg.kind, slot)
+            defs.append(
+                {
+                    "key": key,
+                    "placeholder": "{{" + key + "}}",
+                    "description": (
+                        f"{kind_labels.get(cfg.kind, cfg.kind)} — "
+                        f"{slot_labels.get(slot, slot)} (aktiver Eintrag aus planning_catalog_context)."
+                    ),
+                    "used_by_slugs": slugs_common,
+                }
+            )
+
+    defs.extend(
+        [
+            {
+                "key": "catalog_guidance_block",
+                "placeholder": "{{catalog_guidance_block}}",
+                "description": "Aggregierter Markdown-Block aus aktiven Slots (slug-spezifisches Profil).",
+                "used_by_slugs": slugs_common,
+            },
+            {
+                "key": "catalog_context_json",
+                "placeholder": "{{catalog_context_json}}",
+                "description": "Audit-JSON der gewählten Katalog-Einträge und befüllten Slots.",
+                "used_by_slugs": slugs_common[:3],
+            },
+            {
+                "key": "has_catalog_guidance",
+                "placeholder": "{{has_catalog_guidance}}",
+                "description": "„true“ wenn mindestens ein LLM-Slot gesetzt; sonst leer.",
+                "used_by_slugs": slugs_common[:3],
+            },
+        ]
+    )
+    return {"context": "planning", "placeholders": defs}
+
+
+__all__ = [
+    "merge_planning_prompt_variables",
+    "planning_prompt_placeholder_catalog",
+]

backend/routers/ai_prompts_admin.py

@@ -14,9 +14,15 @@ from auth import require_auth
 from club_tenancy import is_superadmin
 from ai_prompt_context import ExerciseFormAiPromptContext
 from ai_prompt_job import resolve_exercise_form_variables
+from ai_prompt_planning_preview import (
+    PlanningPromptPreviewInput,
+    is_planning_prompt_slug,
+    resolve_planning_prompt_preview_variables,
+)
 from ai_prompt_runtime import render_ai_prompt_template_for_row
 from db import get_cursor, get_db, r2d
 from prompt_resolver import exercise_placeholder_catalog
+from planning_prompt_variables import planning_prompt_placeholder_catalog
 
 router = APIRouter(tags=["admin_ai_prompts"])
 
@@ -62,12 +68,22 @@ class AiPromptUpdateBody(BaseModel):
 
 
 class AiPromptPreviewBody(ExerciseFormAiPromptContext):
-    """Preview-POST: gleiche Felder wie ExerciseFormAiPromptContext (focus_hint, nicht focus_area_hint)."""
+    """Preview-POST: Übungs-KI und Planungs-Prompts."""
+
+    goal_query: Optional[str] = Field(default=None, max_length=2000)
+    user_notes: Optional[str] = Field(default=None, max_length=2000)
+    max_steps: Optional[int] = Field(default=None, ge=2, le=10)
+    search_query: Optional[str] = Field(default=None, max_length=2000)
 
 
 @router.get("/api/admin/ai-prompts/catalog/placeholders")
 def get_ai_prompt_placeholders_catalog(session: dict = Depends(_require_superadmin)):
-    return exercise_placeholder_catalog()
+    exercise = exercise_placeholder_catalog()
+    planning = planning_prompt_placeholder_catalog()
+    return {
+        "context": "all",
+        "placeholders": list(exercise.get("placeholders") or []) + list(planning.get("placeholders") or []),
+    }
 
 
 @router.get("/api/admin/ai-prompts")
@@ -223,6 +239,17 @@ def preview_ai_prompt(prompt_id: int, body: AiPromptPreviewBody, session: dict =
                 vars_map = resolve_exercise_form_variables(cur, slug, body)
             except ValueError as e:
                 raise HTTPException(status_code=400, detail=str(e)) from e
+        elif is_planning_prompt_slug(slug):
+            planning_in = PlanningPromptPreviewInput(
+                goal_query=(body.goal_query or "Mae Geri vom Grundschritt bis zur Kumite-Nähe").strip(),
+                user_notes=(body.user_notes or "").strip(),
+                max_steps=body.max_steps if body.max_steps is not None else 5,
+                search_query=(body.search_query or body.goal_query or "").strip() or None,
+            )
+            try:
+                vars_map = resolve_planning_prompt_preview_variables(cur, slug, planning_in)
+            except ValueError as e:
+                raise HTTPException(status_code=400, detail=str(e)) from e
         elif slug == "pipeline":
             vars_map = {}
             warn = "Pipeline-Slug: keine Kontextsubstitution fuer Vorschau."

backend/routers/catalog_prompt_slots.py

@@ -0,0 +1,97 @@
+"""
+API: Katalog-Prompt-Slots (Stammdaten × Slot-Typ).
+
+Globaler Admin-Katalog (wie catalogs.py) — require_auth + Admin-Rolle, kein TenantContext.
+Eingetragen in backend/scripts/check_access_layer_hints.py EXEMPT_ROUTERS.
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel, Field
+
+from auth import require_auth
+from catalog_prompt_slots import (
+    CATALOG_KINDS,
+    get_catalog_entry_slots,
+    list_slot_type_definitions,
+    upsert_catalog_entry_slots,
+)
+from db import get_cursor, get_db
+
+router = APIRouter(prefix="/api", tags=["catalog_prompt_slots"])
+
+_VALID_KINDS = frozenset(c.kind for c in CATALOG_KINDS)
+
+
+class CatalogPromptSlotsBody(BaseModel):
+    slots: Dict[str, Optional[str]] = Field(default_factory=dict)
+
+
+def _require_admin(session: dict = Depends(require_auth)) -> dict:
+    role = (session.get("role") or "").strip().lower()
+    if role not in ("admin", "superadmin"):
+        raise HTTPException(status_code=403, detail="Nur Admins")
+    return session
+
+
+def _slots_table_ready(cur) -> bool:
+    cur.execute("SELECT to_regclass(%s)::text AS t", ("public.catalog_prompt_slots",))
+    row = cur.fetchone()
+    if not row:
+        return False
+    val = row.get("t") if isinstance(row, dict) else row[0]
+    return val is not None and str(val).strip() != ""
+
+
+@router.get("/catalog-prompt-slot-types")
+def api_list_catalog_prompt_slot_types(session: dict = Depends(_require_admin)):
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        if not _slots_table_ready(cur):
+            raise HTTPException(status_code=503, detail="Tabelle catalog_prompt_slots fehlt.")
+        return {"slot_types": list_slot_type_definitions(cur)}
+
+
+@router.get("/catalog-prompt-slots/{catalog_kind}/{catalog_id}")
+def api_get_catalog_prompt_slots(
+    catalog_kind: str,
+    catalog_id: int,
+    session: dict = Depends(_require_admin),
+):
+    kind = (catalog_kind or "").strip().lower()
+    if kind not in _VALID_KINDS:
+        raise HTTPException(status_code=400, detail=f"Unbekannter catalog_kind: {catalog_kind!r}")
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        if not _slots_table_ready(cur):
+            raise HTTPException(status_code=503, detail="Tabelle catalog_prompt_slots fehlt.")
+        try:
+            return get_catalog_entry_slots(cur, kind, catalog_id)
+        except LookupError as exc:
+            raise HTTPException(status_code=404, detail=str(exc)) from exc
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.put("/catalog-prompt-slots/{catalog_kind}/{catalog_id}")
+def api_put_catalog_prompt_slots(
+    catalog_kind: str,
+    catalog_id: int,
+    body: CatalogPromptSlotsBody,
+    session: dict = Depends(_require_admin),
+):
+    kind = (catalog_kind or "").strip().lower()
+    if kind not in _VALID_KINDS:
+        raise HTTPException(status_code=400, detail=f"Unbekannter catalog_kind: {catalog_kind!r}")
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        if not _slots_table_ready(cur):
+            raise HTTPException(status_code=503, detail="Tabelle catalog_prompt_slots fehlt.")
+        try:
+            return upsert_catalog_entry_slots(cur, kind, catalog_id, body.slots or {})
+        except LookupError as exc:
+            raise HTTPException(status_code=404, detail=str(exc)) from exc
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc

backend/routers/exercise_progression_graphs.py

@@ -4,7 +4,7 @@ Optional Übungsvarianten als Knoten-Endpunkte; Sequenz-Bulk-Anlage.
 AuthZ analog training_plan_templates: Bearbeiten/Löschen wie Übungen; Governance-Übergänge zentral.
 """
 import json
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Mapping, Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel, Field, model_validator
@@ -19,6 +19,7 @@ from club_tenancy import (
     assert_library_content_editable,
     assert_library_content_governance_transition,
     assert_valid_governance_visibility,
+    is_platform_admin,
     library_content_visible_to_profile,
 )
 
@@ -176,6 +177,87 @@ def _assert_variant_for_exercise(cur, exercise_id: int, variant_id: Optional[int
         raise HTTPException(status_code=400, detail="Variante gehört nicht zur gewählten Übung")
 
 
+def _exercise_allowed_in_progression_graph(
+    exercise_row: Mapping[str, Any],
+    *,
+    graph_visibility: str,
+    graph_club_id: Optional[int],
+    profile_id: int,
+    role: str,
+) -> bool:
+    """Prüft, ob eine Übung zur Sichtbarkeit des Progressionsgraphen passt."""
+    ex_vis = (exercise_row.get("visibility") or "private").strip().lower()
+    gvis = (graph_visibility or "private").strip().lower()
+    if gvis == "private":
+        if ex_vis == "official":
+            return True
+        if ex_vis == "club":
+            return True
+        if ex_vis == "private":
+            if is_platform_admin(role):
+                return True
+            try:
+                return int(exercise_row.get("created_by") or 0) == int(profile_id)
+            except (TypeError, ValueError):
+                return False
+        return False
+    if gvis == "club":
+        if ex_vis == "official":
+            return True
+        if ex_vis != "club":
+            return False
+        ex_club = exercise_row.get("club_id")
+        if ex_club is None:
+            return False
+        if graph_club_id is None:
+            return True
+        return int(ex_club) == int(graph_club_id)
+    return ex_vis == "official"
+
+
+def _assert_exercises_allowed_in_graph(
+    cur,
+    graph_id: int,
+    profile_id: int,
+    role: str,
+    *exercise_ids: int,
+) -> None:
+    """400 wenn eine Übung nicht zur Graph-Sichtbarkeit passt."""
+    row = _graph_row(cur, graph_id)
+    gvis = (row.get("visibility") or "private").strip().lower()
+    gclub_raw = row.get("club_id")
+    gclub = int(gclub_raw) if gclub_raw is not None else None
+    unique = list(dict.fromkeys(exercise_ids))
+    if not unique:
+        return
+    ph = ",".join(["%s"] * len(unique))
+    cur.execute(
+        f"SELECT id, title, visibility, club_id, created_by FROM exercises WHERE id IN ({ph})",
+        tuple(unique),
+    )
+    by_id = {int(r2d(r)["id"]): r2d(r) for r in cur.fetchall()}
+    for eid in unique:
+        ex = by_id.get(int(eid))
+        if not ex:
+            continue
+        if _exercise_allowed_in_progression_graph(
+            ex,
+            graph_visibility=gvis,
+            graph_club_id=gclub,
+            profile_id=profile_id,
+            role=role,
+        ):
+            continue
+        title = (ex.get("title") or "").strip() or f"#{eid}"
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                f"Übung „{title}“ (Sichtbarkeit: {ex.get('visibility') or 'private'}) "
+                f"passt nicht zum Progressionsgraphen ({gvis})."
+            ),
+        )
+
+
 def _insert_edge_row(
     cur,
     graph_id: int,
@@ -312,6 +394,22 @@ def _collect_graph_referenced_exercise_ids(cur, graph_id: int) -> set[int]:
     return ids
 
 
+def _graph_promotion_transition(graph_visibility: str, target_visibility: str) -> Optional[tuple[str, ...]]:
+    """
+    Erlaubte Graph-Promotions und welche Übungs-Sichtbarkeiten mit angehoben werden müssen.
+
+    Returns None wenn kein Übungs-Promotion-Hinweis nötig.
+    """
+    gvis = (graph_visibility or "private").strip().lower()
+    tvis = (target_visibility or "").strip().lower()
+    transitions: Dict[tuple[str, str], tuple[str, ...]] = {
+        ("private", "club"): ("private",),
+        ("private", "official"): ("private", "club"),
+        ("club", "official"): ("private", "club"),
+    }
+    return transitions.get((gvis, tvis))
+
+
 @router.get("/exercise-progression-graphs/{graph_id}/visibility-promotion-candidates")
 def list_visibility_promotion_candidates(
     graph_id: int,
@@ -319,7 +417,9 @@ def list_visibility_promotion_candidates(
     tenant: TenantContext = Depends(get_tenant_context),
 ):
     """
-    Private Übungen im Graph, die bei Promotion des Graphen mit angehoben werden müssten.
+    Übungen im Graph, die bei Promotion des Graphen mit angehoben werden müssten.
+
+    Unterstützt: private→club, private→official, club→official.
     """
     profile_id = tenant.profile_id
     role = tenant.global_role
@@ -327,11 +427,13 @@ def list_visibility_promotion_candidates(
         cur = get_cursor(conn)
         row = _require_graph_read(cur, graph_id, profile_id, role)
         graph_vis = (row.get("visibility") or "private").strip().lower()
-        if graph_vis != "private" or target_visibility != "club":
+        target_vis = (target_visibility or "club").strip().lower()
+        need_vis = _graph_promotion_transition(graph_vis, target_vis)
+        if not need_vis:
             return {
                 "graph_id": graph_id,
                 "graph_visibility": graph_vis,
-                "target_visibility": target_visibility,
+                "target_visibility": target_vis,
                 "exercises": [],
             }
         ref_ids = _collect_graph_referenced_exercise_ids(cur, graph_id)
@@ -339,19 +441,20 @@ def list_visibility_promotion_candidates(
             return {
                 "graph_id": graph_id,
                 "graph_visibility": graph_vis,
-                "target_visibility": target_visibility,
+                "target_visibility": target_vis,
                 "exercises": [],
             }
+        vis_placeholders = ",".join(["%s"] * len(need_vis))
         ph = ",".join(["%s"] * len(ref_ids))
         cur.execute(
             f"""
             SELECT id, title, visibility, club_id, created_by
             FROM exercises
             WHERE id IN ({ph})
-              AND LOWER(TRIM(COALESCE(visibility, ''))) = 'private'
+              AND LOWER(TRIM(COALESCE(visibility, ''))) IN ({vis_placeholders})
             ORDER BY title
             """,
-            list(ref_ids),
+            list(ref_ids) + list(need_vis),
         )
         exercises = []
         for ex in cur.fetchall():
@@ -359,8 +462,10 @@ def list_visibility_promotion_candidates(
             if not library_content_visible_to_profile(
                 cur,
                 profile_id,
+                (exd.get("visibility") or "private").strip().lower(),
+                exd.get("club_id"),
+                exd.get("created_by"),
                 role,
-                exd,
             ):
                 continue
             exercises.append(
@@ -373,7 +478,7 @@ def list_visibility_promotion_candidates(
     return {
         "graph_id": graph_id,
         "graph_visibility": graph_vis,
-        "target_visibility": target_visibility,
+        "target_visibility": target_vis,
         "exercises": exercises,
     }
 
@@ -565,6 +670,9 @@ def create_progression_edge(
         cur = get_cursor(conn)
         _require_graph_write(cur, graph_id, profile_id, role)
         _assert_exercises_exist(cur, body.from_exercise_id, body.to_exercise_id)
+        _assert_exercises_allowed_in_graph(
+            cur, graph_id, profile_id, role, body.from_exercise_id, body.to_exercise_id
+        )
         fv = body.from_exercise_variant_id
         tv = body.to_exercise_variant_id
         _assert_variant_for_exercise(cur, body.from_exercise_id, fv)
@@ -613,6 +721,7 @@ def create_progression_sequence(
 
         ex_ids = [s.exercise_id for s in steps]
         _assert_exercises_exist(cur, *ex_ids)
+        _assert_exercises_allowed_in_graph(cur, graph_id, profile_id, role, *ex_ids)
 
         try:
             for i in range(n_seg):

backend/routers/planning_exercise_suggest.py

@@ -7,6 +7,7 @@ from db import get_db, get_cursor
 from tenant_context import TenantContext, get_tenant_context
 from planning_exercise_suggest import PlanningExerciseSuggestRequest, suggest_planning_exercises
 from planning_exercise_path_builder import ProgressionPathSuggestRequest, suggest_progression_path
+from planning_llm_usage import planning_llm_call_meter
 from account_lifecycle import assert_min_account_state
 from capabilities import probe_capability
 from club_features import (
@@ -46,19 +47,25 @@ def post_planning_exercise_suggest(
         )
     with get_db() as conn:
         cur = get_cursor(conn)
+        with planning_llm_call_meter() as llm_meter:
             result = suggest_planning_exercises(cur, tenant=tenant, body=body)
-        if uses_ai:
+        if uses_ai and llm_meter.count > 0:
             usage = consume_club_feature_with_usage(
                 feature_id="ai_calls",
                 club_id=club_id,
                 profile_id=tenant.profile_id,
                 portal_role=tenant.global_role,
                 action="planning_suggest",
+                amount=llm_meter.count,
                 cur=cur,
                 tenant=tenant,
                 conn=conn,
             )
             result = merge_feature_usage_into_response(result, usage)
+            if isinstance(result, dict):
+                result["llm_call_count"] = llm_meter.count
+        elif uses_ai and isinstance(result, dict):
+            result["llm_call_count"] = 0
         return result
 
 
@@ -70,7 +77,6 @@ def post_progression_path_suggest(
     uses_ai = (
         body.include_llm_intent
         or body.include_llm_path_qa
-        or body.include_ai_gap_fill
         or body.include_llm_roadmap
         or body.include_llm_start_target
         or (body.start_target_only and body.include_llm_start_target)
@@ -98,17 +104,23 @@ def post_progression_path_suggest(
         )
     with get_db() as conn:
         cur = get_cursor(conn)
+        with planning_llm_call_meter() as llm_meter:
             result = suggest_progression_path(cur, tenant=tenant, body=body)
-        if uses_ai:
+        if uses_ai and llm_meter.count > 0:
             usage = consume_club_feature_with_usage(
                 feature_id="ai_calls",
                 club_id=club_id,
                 profile_id=tenant.profile_id,
                 portal_role=tenant.global_role,
                 action="progression_path_suggest",
+                amount=llm_meter.count,
                 cur=cur,
                 tenant=tenant,
                 conn=conn,
             )
             result = merge_feature_usage_into_response(result, usage)
+            if isinstance(result, dict):
+                result["llm_call_count"] = llm_meter.count
+        elif uses_ai and isinstance(result, dict):
+            result["llm_call_count"] = 0
         return result

backend/scripts/check_access_layer_hints.py

@@ -29,6 +29,7 @@ EXEMPT_ROUTERS: frozenset[str] = frozenset(
         "admin_user_content.py",  # Superadmin Moderation nutzerangelegter Inhalte; require_auth + is_superadmin — kein Vereinsmandant
         "admin_rights.py",  # Superadmin Rollen/Rechte (Capabilities, Kontingent-Bypass, Pläne); require_auth + is_superadmin — kein Vereinsmandant
         "catalogs.py",
+        "catalog_prompt_slots.py",  # Admin Stammdaten KI-Prompt-Slots; require_auth + admin/superadmin — globaler Katalog, kein Vereinsmandant
         "skills.py",
         "maturity_models.py",
         "matrix_stack_bundle.py",

backend/tests/test_ai_prompt_planning_preview.py

@@ -0,0 +1,89 @@
+"""Admin-Vorschau für Planungs-Prompt-Slugs."""
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from ai_prompt_planning_preview import (
+    PLANNING_PROMPT_SLUGS,
+    PlanningPromptPreviewInput,
+    is_planning_prompt_slug,
+    resolve_planning_prompt_preview_variables,
+)
+
+
+def test_is_planning_prompt_slug():
+    assert is_planning_prompt_slug("planning_progression_roadmap")
+    assert is_planning_prompt_slug("PLANNING_EXERCISE_PATH_QA")
+    assert not is_planning_prompt_slug("exercise_summary")
+    assert not is_planning_prompt_slug("")
+
+
+def test_resolve_roadmap_preview_variables():
+    body = PlanningPromptPreviewInput(goal_query="Mae Geri Basics", max_steps=4)
+    vars_map = resolve_planning_prompt_preview_variables(
+        MagicMock(),
+        "planning_progression_roadmap",
+        body,
+    )
+    assert vars_map["goal_query"] == "Mae Geri Basics"
+    assert vars_map["max_steps"] == "4"
+    assert "goal_analysis_json" in vars_map
+    assert "semantic_brief_json" in vars_map
+
+
+def test_resolve_stage_spec_includes_intent_context():
+    body = PlanningPromptPreviewInput(user_notes="Breitensport")
+    vars_map = resolve_planning_prompt_preview_variables(
+        MagicMock(),
+        "planning_progression_stage_spec",
+        body,
+    )
+    assert "intent_context_json" in vars_map
+    assert "major_steps_json" in vars_map
+
+
+@patch("ai_prompt_planning_preview._load_catalog_variables")
+def test_resolve_search_intent_includes_catalogs(mock_catalog):
+    mock_catalog.return_value = {
+        "skills_catalog_json": "[]",
+        "focus_areas_catalog_json": "[]",
+        "training_types_catalog_json": "[]",
+        "style_directions_catalog_json": "[]",
+        "target_groups_catalog_json": "[]",
+    }
+    body = PlanningPromptPreviewInput(search_query="Mae Geri nächster Schritt")
+    vars_map = resolve_planning_prompt_preview_variables(
+        MagicMock(),
+        "planning_exercise_search_intent",
+        body,
+    )
+    assert vars_map["search_query"] == "Mae Geri nächster Schritt"
+    assert vars_map["skills_catalog_json"] == "[]"
+
+
+def test_non_planning_slug_raises():
+    with pytest.raises(ValueError, match="Kein Planungs-Prompt-Slug"):
+        resolve_planning_prompt_preview_variables(
+            MagicMock(),
+            "exercise_summary",
+            PlanningPromptPreviewInput(),
+        )
+
+
+def test_all_registered_slugs_resolve():
+    for slug in PLANNING_PROMPT_SLUGS:
+        with patch("ai_prompt_planning_preview._load_catalog_variables") as mock_catalog:
+            mock_catalog.return_value = {
+                "skills_catalog_json": "[]",
+                "focus_areas_catalog_json": "[]",
+                "training_types_catalog_json": "[]",
+                "style_directions_catalog_json": "[]",
+                "target_groups_catalog_json": "[]",
+            }
+            vars_map = resolve_planning_prompt_preview_variables(
+                MagicMock(),
+                slug,
+                PlanningPromptPreviewInput(),
+            )
+            assert isinstance(vars_map, dict)
+            assert len(vars_map) >= 1

backend/tests/test_catalog_slot_fallbacks.py

@@ -0,0 +1,38 @@
+"""Tests Namens-Fallback für Katalog-Prompt-Slots."""
+from catalog_slot_fallbacks import get_fallback_slots_for_entry, merge_stored_slots_with_fallbacks
+from catalog_prompt_slots import _resolve_entry_slot_values
+
+
+def test_karate_fallback_has_path_qa():
+    pack = get_fallback_slots_for_entry("focus_area", "Karate")
+    assert "Kohärente Progression" in pack.get("hints_on_path_qa", "")
+
+
+def test_db_value_overrides_fallback():
+    merged = merge_stored_slots_with_fallbacks(
+        {"hints_on_path_qa": "Eigener QS-Text."},
+        catalog_kind="focus_area",
+        name="Karate",
+        stammdaten_description="Traditionelles Karate",
+    )
+    assert merged["hints_on_path_qa"] == "Eigener QS-Text."
+
+
+def test_empty_db_uses_karate_fallback():
+    merged = _resolve_entry_slot_values(
+        {},
+        {"name": "Karate", "description": "Traditionelles Karate"},
+        "focus_area",
+    )
+    assert "Kihon-Progression" in merged["description"] or "Technik-Curriculum" in merged["description"]
+    assert "Kohärente Progression" in merged["hints_on_path_qa"]
+
+
+def test_gewaltschutz_fallback_no_kumite():
+    merged = _resolve_entry_slot_values(
+        {},
+        {"name": "Gewaltschutz", "description": "Gewaltprävention"},
+        "focus_area",
+    )
+    assert "Deeskalation" in merged["hints_on_path_qa"]
+    assert "Kumite-Tiefe" in merged["anti_patterns"]

backend/tests/test_exercise_progression_graph_visibility.py

@@ -0,0 +1,80 @@
+"""Sichtbarkeit: Progressionsgraph ↔ Übungen (Promotion, Kanten, Match)."""
+from routers.exercise_progression_graphs import (
+    _exercise_allowed_in_progression_graph,
+    _graph_promotion_transition,
+)
+
+
+def test_graph_promotion_transition_private_to_club():
+    assert _graph_promotion_transition("private", "club") == ("private",)
+
+
+def test_graph_promotion_transition_private_to_official():
+    assert _graph_promotion_transition("private", "official") == ("private", "club")
+
+
+def test_graph_promotion_transition_club_to_official():
+    assert _graph_promotion_transition("club", "official") == ("private", "club")
+
+
+def test_graph_promotion_transition_noop():
+    assert _graph_promotion_transition("club", "club") is None
+    assert _graph_promotion_transition("official", "club") is None
+    assert _graph_promotion_transition("private", "private") is None
+
+
+def test_club_graph_rejects_private_exercise():
+    assert not _exercise_allowed_in_progression_graph(
+        {"visibility": "private", "club_id": None, "created_by": 1},
+        graph_visibility="club",
+        graph_club_id=5,
+        profile_id=1,
+        role="trainer",
+    )
+
+
+def test_club_graph_accepts_matching_club_exercise():
+    assert _exercise_allowed_in_progression_graph(
+        {"visibility": "club", "club_id": 5, "created_by": 2},
+        graph_visibility="club",
+        graph_club_id=5,
+        profile_id=1,
+        role="trainer",
+    )
+
+
+def test_club_graph_accepts_official_exercise():
+    assert _exercise_allowed_in_progression_graph(
+        {"visibility": "official", "club_id": None, "created_by": 99},
+        graph_visibility="club",
+        graph_club_id=5,
+        profile_id=1,
+        role="trainer",
+    )
+
+
+def test_private_graph_accepts_own_private_exercise():
+    assert _exercise_allowed_in_progression_graph(
+        {"visibility": "private", "club_id": None, "created_by": 7},
+        graph_visibility="private",
+        graph_club_id=None,
+        profile_id=7,
+        role="trainer",
+    )
+
+
+def test_official_graph_requires_official_exercise():
+    assert not _exercise_allowed_in_progression_graph(
+        {"visibility": "club", "club_id": 5, "created_by": 2},
+        graph_visibility="official",
+        graph_club_id=None,
+        profile_id=1,
+        role="trainer",
+    )
+    assert _exercise_allowed_in_progression_graph(
+        {"visibility": "official", "club_id": None, "created_by": 2},
+        graph_visibility="official",
+        graph_club_id=None,
+        profile_id=1,
+        role="trainer",
+    )

backend/tests/test_planning_catalog_prompt_snippets.py

@@ -0,0 +1,168 @@
+"""Tests Katalog-Prompt-Slots (H2)."""
+from unittest.mock import MagicMock
+
+from catalog_prompt_slots import (
+    build_catalog_guidance_for_prompt,
+    pick_active_catalog_item,
+    placeholder_key,
+    resolve_catalog_prompt_variables,
+)
+from planning_catalog_context import PlanningCatalogContextItem, ProgressionPlanningCatalogContext
+from planning_prompt_variables import merge_planning_prompt_variables
+
+
+def _mock_cur(
+    rows_by_table=None,
+    slots_by_kind_id=None,
+    slot_types_ready=True,
+):
+    rows_by_table = rows_by_table or {}
+    slots_by_kind_id = slots_by_kind_id or {}
+
+    cur = MagicMock()
+
+    def execute(sql, params=None):
+        sql_l = (sql or "").lower()
+        if "to_regclass" in sql_l:
+            cur.fetchone.return_value = {"t": "catalog_prompt_slot_types" if slot_types_ready else None}
+            return
+        if "from catalog_prompt_slot_types" in sql_l:
+            cur.fetchall.return_value = []
+            return
+        if "from catalog_prompt_slots" in sql_l:
+            kind, cid = params[0], int(params[1])
+            slot_map = slots_by_kind_id.get((kind, cid), {})
+            cur.fetchall.return_value = [
+                {"slot_key": k, "content": v} for k, v in slot_map.items()
+            ]
+            return
+        for table, rows in rows_by_table.items():
+            if f"from {table}" in sql_l:
+                item_id = int(params[0])
+                raw = rows.get(item_id)
+                if raw is None:
+                    cur.fetchone.return_value = None
+                elif isinstance(raw, dict):
+                    cur.fetchone.return_value = {
+                        "id": item_id,
+                        "name": raw.get("name", ""),
+                        "description": raw.get("description", ""),
+                    }
+                else:
+                    cur.fetchone.return_value = {
+                        "id": item_id,
+                        "name": str(raw),
+                        "description": "",
+                    }
+                return
+        cur.fetchone.return_value = None
+        cur.fetchall.return_value = []
+
+    cur.execute.side_effect = execute
+    return cur
+
+
+def test_pick_active_catalog_item_primary_wins():
+    items = [
+        PlanningCatalogContextItem(id=1, is_primary=False, weight=0.9),
+        PlanningCatalogContextItem(id=2, is_primary=True, weight=0.5),
+    ]
+    assert pick_active_catalog_item(items).id == 2
+
+
+def test_granular_placeholder_focus_area_hints_on_path_qa():
+    cur = _mock_cur(
+        rows_by_table={"focus_areas": {4: {"name": "Gewaltschutz"}}},
+        slots_by_kind_id={
+            ("focus_area", 4): {
+                "description": "Planung zielt auf Prävention und Deeskalation.",
+                "hints_on_path_qa": "Lücken sind fehlende Deeskalations-Stufen.",
+                "anti_patterns": "Nicht nach Kumite-Tiefe bewerten.",
+            }
+        },
+    )
+    catalog = ProgressionPlanningCatalogContext(
+        focus_areas=[PlanningCatalogContextItem(id=4, is_primary=True)],
+    )
+    resolved = resolve_catalog_prompt_variables(cur, catalog, slug="planning_exercise_path_qa")
+    assert "Deeskalation" in resolved[placeholder_key("focus_area", "hints_on_path_qa")]
+    assert "Deeskalation" in resolved["catalog_guidance_block"]
+    assert resolved["has_catalog_guidance"] == "true"
+
+
+def test_unknown_focus_uses_default_description_pack():
+    cur = _mock_cur(
+        rows_by_table={
+            "focus_areas": {
+                4: {
+                    "name": "Sonderfokus Alpha",
+                    "description": "Kurze Stammdaten-Beschreibung",
+                }
+            }
+        },
+        slots_by_kind_id={("focus_area", 4): {}},
+    )
+    catalog = ProgressionPlanningCatalogContext(
+        focus_areas=[PlanningCatalogContextItem(id=4, is_primary=True)],
+    )
+    resolved = resolve_catalog_prompt_variables(cur, catalog)
+    desc = resolved[placeholder_key("focus_area", "description")]
+    assert "Technik- oder Themen-Curriculum" in desc
+    assert resolved[placeholder_key("focus_area", "hints_on_path_qa")]
+
+
+def test_empty_without_catalog():
+    cur = MagicMock()
+    out = build_catalog_guidance_for_prompt(cur, None)
+    assert out["has_catalog_guidance"] is False
+    assert out["catalog_guidance_block"] == ""
+
+
+def test_unknown_entry_gets_default_technique_fallback():
+    cur = _mock_cur(rows_by_table={"focus_areas": {99: {"name": "Unbekannter Fokus XYZ"}}})
+    catalog = ProgressionPlanningCatalogContext(
+        focus_areas=[PlanningCatalogContextItem(id=99, is_primary=True)],
+    )
+    out = build_catalog_guidance_for_prompt(cur, catalog)
+    assert out["has_catalog_guidance"] is True
+    assert "Unbekannter Fokus XYZ" in out["catalog_context_json"]
+    assert "Zwischenstufen" in out["catalog_guidance_block"] or "Progression" in out["catalog_guidance_block"]
+
+
+def test_merge_planning_prompt_variables_granular_keys():
+    cur = _mock_cur(
+        rows_by_table={"focus_areas": {4: {"name": "Gewaltschutz"}}},
+        slots_by_kind_id={
+            ("focus_area", 4): {"hints_on_path_qa": "Deeskalation und Grenzen."}
+        },
+    )
+    catalog = ProgressionPlanningCatalogContext(
+        focus_areas=[PlanningCatalogContextItem(id=4, is_primary=True)],
+    )
+    merged = merge_planning_prompt_variables(
+        cur,
+        {"goal_query": "Deeskalation Kinder"},
+        catalog=catalog,
+        slug="planning_exercise_path_qa",
+    )
+    assert merged[placeholder_key("focus_area", "hints_on_path_qa")].startswith("Deeskalation")
+    assert merged["has_catalog_guidance"] == "true"
+
+
+def test_priority_order_in_guidance_block():
+    cur = _mock_cur(
+        rows_by_table={
+            "focus_areas": {1: {"name": "Gewaltschutz"}},
+            "training_types": {2: {"name": "Breitensport"}},
+        },
+        slots_by_kind_id={
+            ("focus_area", 1): {"description": "Fokus-Text"},
+            ("training_type", 2): {"description": "Stil-Text"},
+        },
+    )
+    catalog = ProgressionPlanningCatalogContext(
+        focus_areas=[PlanningCatalogContextItem(id=1, is_primary=True)],
+        training_types=[PlanningCatalogContextItem(id=2, is_primary=True)],
+    )
+    block = build_catalog_guidance_for_prompt(cur, catalog)["catalog_guidance_block"]
+    assert block.index("Primärfokus") < block.index("Trainingsstil")

backend/tests/test_planning_deterministic_quality_score.py

@@ -3,19 +3,28 @@ from planning_exercise_path_qa import compute_deterministic_path_quality_score
 
 
 def test_deterministic_quality_score_penalizes_off_topic():
-    base = compute_deterministic_path_quality_score(gaps=[], off_topic_steps=[])
+    steps = [{"roadmap_major_step_index": 0, "exercise_id": 1}]
+    base = compute_deterministic_path_quality_score(gaps=[], off_topic_steps=[], steps=steps)
     with_off = compute_deterministic_path_quality_score(
         gaps=[],
         off_topic_steps=[{"roadmap_major_step_index": 1}],
+        steps=steps,
     )
     assert with_off < base
 
 
 def test_deterministic_quality_score_penalizes_empty_slots():
-    base = compute_deterministic_path_quality_score(gaps=[], off_topic_steps=[], steps=[])
+    filled = [{"roadmap_major_step_index": 0, "exercise_id": 1}]
+    base = compute_deterministic_path_quality_score(gaps=[], off_topic_steps=[], steps=filled)
     with_empty = compute_deterministic_path_quality_score(
         gaps=[],
         off_topic_steps=[],
-        steps=[{"exercise_id": None}, {"exercise_id": 1}],
+        steps=[{"roadmap_major_step_index": 0, "exercise_id": None}, {"roadmap_major_step_index": 1, "exercise_id": 2}],
+    )
+    all_empty = compute_deterministic_path_quality_score(
+        gaps=[],
+        off_topic_steps=[],
+        steps=[{"roadmap_major_step_index": 0, "exercise_id": None}] * 4,
     )
     assert with_empty < base
+    assert all_empty <= 0.15

backend/tests/test_planning_llm_usage.py

@@ -0,0 +1,94 @@
+"""LLM-Zählung für Planungs-APIs (P1-C2)."""
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from planning_llm_usage import (
+    current_planning_llm_call_counter,
+    planning_llm_call_meter,
+    record_planning_llm_call,
+)
+
+
+def test_meter_inactive_by_default():
+    assert current_planning_llm_call_counter() is None
+    record_planning_llm_call(3)
+    assert current_planning_llm_call_counter() is None
+
+
+def test_meter_counts_within_scope():
+    with planning_llm_call_meter() as meter:
+        record_planning_llm_call(1)
+        record_planning_llm_call(2)
+        assert meter.count == 3
+
+
+def test_openrouter_increments_active_meter():
+    from openrouter_chat import openrouter_chat_completion
+
+    fake_resp = MagicMock()
+    fake_resp.status_code = 200
+    fake_resp.json.return_value = {
+        "choices": [{"message": {"content": "ok"}, "finish_reason": "stop"}],
+    }
+
+    with planning_llm_call_meter() as meter:
+        with patch("openrouter_chat.httpx.Client") as client_cls:
+            client = MagicMock()
+            client.__enter__.return_value = client
+            client.post.return_value = fake_resp
+            client_cls.return_value = client
+            out = openrouter_chat_completion(
+                api_key="test-key",
+                model="test/model",
+                user_content="hello",
+            )
+        assert out == "ok"
+        assert meter.count == 1
+
+
+def test_openrouter_skips_meter_on_http_error():
+    from openrouter_chat import OpenRouterError, openrouter_chat_completion
+
+    fake_resp = MagicMock()
+    fake_resp.status_code = 500
+    fake_resp.json.return_value = {"error": {"message": "fail"}}
+    fake_resp.text = "fail"
+
+    with planning_llm_call_meter() as meter:
+        with patch("openrouter_chat.httpx.Client") as client_cls:
+            client = MagicMock()
+            client.__enter__.return_value = client
+            client.post.return_value = fake_resp
+            client_cls.return_value = client
+            with pytest.raises(OpenRouterError):
+                openrouter_chat_completion(
+                    api_key="test-key",
+                    model="test/model",
+                    user_content="hello",
+                )
+        assert meter.count == 0
+
+
+def test_uses_ai_gap_fill_not_counted_without_openrouter():
+    """Regression: Gap-Fill-Flag allein löst keinen OpenRouter-Aufruf aus."""
+    from planning_exercise_path_builder import ProgressionPathSuggestRequest
+
+    body = ProgressionPathSuggestRequest(
+        query="Mae Geri Progression",
+        include_llm_intent=False,
+        include_llm_path_qa=False,
+        include_llm_roadmap=False,
+        include_llm_start_target=False,
+        include_ai_gap_fill=True,
+        evaluate_only=True,
+        evaluate_steps=[],
+    )
+    uses_ai = (
+        body.include_llm_intent
+        or body.include_llm_path_qa
+        or body.include_llm_roadmap
+        or body.include_llm_start_target
+        or (body.start_target_only and body.include_llm_start_target)
+    )
+    assert uses_ai is False

backend/tests/test_planning_path_qa_split.py

@@ -0,0 +1,62 @@
+"""Getrennte Roadmap- vs. Besetzungs-QS."""
+from planning_exercise_path_qa import (
+    build_assignment_qa_snapshot,
+    build_path_qa_summary,
+    compute_assignment_quality_score,
+    merge_path_quality_scores,
+)
+
+
+def _empty_steps(n: int):
+    return [{"roadmap_major_step_index": i, "exercise_id": None} for i in range(n)]
+
+
+def test_assignment_quality_all_empty_slots_is_low():
+    steps = _empty_steps(5)
+    score = compute_assignment_quality_score(steps=steps, off_topic_steps=[], gaps=[])
+    assert score <= 0.15
+
+
+def test_assignment_quality_all_filled_is_high():
+    steps = [{"roadmap_major_step_index": i, "exercise_id": i + 1} for i in range(5)]
+    score = compute_assignment_quality_score(steps=steps, off_topic_steps=[], gaps=[])
+    assert score >= 0.9
+
+
+def test_build_path_qa_summary_caps_llm_score_when_slots_empty():
+    steps = _empty_steps(4)
+    summary = build_path_qa_summary(
+        gaps=[],
+        bridge_inserts=[],
+        ai_proposals=[],
+        off_topic_steps=[],
+        stripped_off_topic=[],
+        llm_qa={
+            "overall_ok": True,
+            "quality_score": 0.88,
+            "topic_coverage": "Roadmap deckt Ziel gut ab",
+            "issues": [],
+            "recommendations": ["Feinschliff Stufe 3"],
+        },
+        llm_applied=True,
+        steps=steps,
+    )
+    assert summary["roadmap_qa"]["quality_score"] == 0.88
+    assert summary["assignment_qa"]["empty_slot_count"] == 4
+    assert summary["assignment_qa"]["quality_score"] <= 0.15
+    assert summary["quality_score"] <= 0.15
+    assert summary["overall_ok"] is False
+
+
+def test_merge_path_quality_uses_minimum():
+    assert merge_path_quality_scores(
+        {"quality_score": 0.88},
+        {"quality_score": 0.12},
+    ) == 0.12
+
+
+def test_assignment_snapshot_reports_empty_slots():
+    snap = build_assignment_qa_snapshot(steps=_empty_steps(3), off_topic_steps=[], gaps=[])
+    assert snap["empty_slot_count"] == 3
+    assert snap["overall_ok"] is False
+    assert any("ohne Übung" in issue for issue in snap["issues"])

backend/tests/test_planning_problematic_slots.py

@@ -2,6 +2,7 @@
 from planning_exercise_path_builder import (
     _parse_slot_refs_from_text,
     _problematic_slots_from_path_qa,
+    _slot_auto_select_ai,
     _slot_auto_select_library,
     _slot_suggestion_accepted,
 )
@@ -113,6 +114,27 @@ def test_slot_auto_select_requires_higher_score():
     )
 
 
+def test_slot_auto_select_empty_slot_requires_good_fit():
+    assert not _slot_auto_select_library(
+        baseline_slot_score=None,
+        proposed_slot_score=0.35,
+        baseline_exercise_id=None,
+        proposed_exercise_id=2,
+    )
+    assert _slot_auto_select_library(
+        baseline_slot_score=None,
+        proposed_slot_score=0.55,
+        baseline_exercise_id=None,
+        proposed_exercise_id=2,
+    )
+
+
+def test_slot_auto_select_ai_when_library_not_selected():
+    assert _slot_auto_select_ai(library_auto_select=False, has_ai=True)
+    assert not _slot_auto_select_ai(library_auto_select=True, has_ai=True)
+    assert not _slot_auto_select_ai(library_auto_select=False, has_ai=False)
+
+
 def test_off_topic_slot_gap_spec_for_filled_slot():
     from planning_exercise_path_builder import _build_off_topic_slot_gap_spec
 

backend/version.py

@@ -1,8 +1,8 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.233"
+APP_VERSION = "0.8.237"
 BUILD_DATE = "2026-05-22"
-DB_SCHEMA_VERSION = "20260607090"
+DB_SCHEMA_VERSION = "20260607094"
 
 MODULE_VERSIONS = {
     "legal_documents": "1.4.0",  # Admin: Live-Vorschau pro Abschnitt + modale Vollvorschau (Editor + Dokumentenliste)
@@ -29,7 +29,7 @@ MODULE_VERSIONS = {
     "media_lifecycle": "1.1.0",  # P-11: Retention-Job ueberspringt Legal-Hold-Assets
     "admin_ai_skill_retrieval": "1.0.0",  # Superadmin CRUD /api/admin/ai-skill-retrieval-profiles (Migration 068)
     "exercise_enrichment_admin": "1.1.1",  # Preview max 3/Request + parallel LLM (Gateway-504 vermeiden)
-    "admin_ai_prompts": "1.0.3",  # Migration 070: openrouter_model; PUT/Liste/Detail
+    "admin_ai_prompts": "1.0.5",  # H2: granulare Katalog-Slot-Platzhalter im Katalog
     "ai_prompt_job": "0.2.1",  # want_instructions; run_exercise_form_ai_suggestion
     "ai_prompt_context": "0.2.0",  # preparation/trainer_notes; has_instruction_source_text
     "ai_prompt_runtime": "0.2.2",  # Slug planning_exercise_expectation_profile
@@ -53,6 +53,40 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.237",
+        "date": "2026-05-22",
+        "changes": [
+            "Migration 094: catalog_prompt_slots vollständig befüllt (Karate, SV, alle Trainingsstile/Zielgruppen).",
+            "catalog_slot_fallbacks: Namens-Fallback bis Admin-Override — gleiche Qualität wie H1-Registry.",
+        ],
+    },
+    {
+        "version": "0.8.236",
+        "date": "2026-05-22",
+        "changes": [
+            "Stammdaten-Katalog: CatalogPromptSlotsEditor für Fokus, Trainingsstil, Zielgruppe, Stilrichtung.",
+            "Migration 093: ai_prompts mit granularen Slot-Platzhaltern (focus_area_hints_on_path_qa etc.).",
+        ],
+    },
+    {
+        "version": "0.8.235",
+        "date": "2026-05-22",
+        "changes": [
+            "Planungs-KI H2: catalog_prompt_slot_types + catalog_prompt_slots — Slot-Werte pro Katalog-Eintrag.",
+            "Granulare Platzhalter focus_area_hints_on_progression etc.; Resolver catalog_prompt_slots.py.",
+            "Admin-API GET/PUT /api/catalog-prompt-slots/{kind}/{id}; H1-Registry entfernt.",
+        ],
+    },
+    {
+        "version": "0.8.234",
+        "date": "2026-05-22",
+        "changes": [
+            "Planungs-KI H1: Katalog-Snippets (planning_catalog_prompt_snippets) + zentrale Platzhalter (planning_prompt_variables).",
+            "Pfad-QS, Roadmap, Stufenspec: {{catalog_guidance_block}} aus Trainer-Katalog; Migration 091.",
+            "Admin: Planungs-Platzhalter-Katalog; Preview mit optional planning_catalog_context.",
+        ],
+    },
     {
         "version": "0.8.226",
         "date": "2026-05-22",

docs/HANDOVER.md

@@ -1,7 +1,7 @@
 # Shinkan Jinkendo – Entwicklungsstand & Handover
 
-**Stand:** 2026-05-22  
-**App-Version / DB-Schema:** App **`0.8.233`** (Planungs-KI F11–F14, Katalog-Kontext); DB siehe **`backend/version.py`** (`DB_SCHEMA_VERSION`, Migration **088**).
+**Stand:** 2026-05-22 (F15 Graph-Match & getrennte Pfad-QS, lokal nach **0.8.233**)  
+**App-Version / DB-Schema:** App **`0.8.233`** (Planungs-KI F11–F14, Katalog-Kontext); **F15** siehe §2.8 — DB unverändert (`DB_SCHEMA_VERSION`, Migration **088**).
 
 Diese Datei ist die **Einstiegs-Doku für neue Chat-Sessions**: Anforderungen im Detail stehen in `.claude/docs/` (siehe unten); hier der **implementierte Stand**, **Medien-Meilenstein** und **sinnvolle nächste Schritte**.
 
@@ -114,11 +114,25 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 | **F12** | Post-Match-Gate, LLM-QA nach Rematch, Gap-Timing, `roadmap_unfilled`-Sync | ✅ **0.8.231–0.8.232** |
 | **F13** | **`planning_catalog_context`** (Fokus/Stil/TT/ZG) im Match + Graph-Artefakt | ✅ **0.8.233** |
 | **F14** | **`ProgressionGraphEditor`** — Slot-UI + Planungskontext-Dropdowns | ✅ **0.8.233** |
+| **F15** | Unified Slot-Review (Match-Dialog), getrennte Pfad-QS, `findings_stale` | ✅ lokal (nach 0.8.233) |
 | **H1** | Katalog-Prompt-Snippets (modulare LLM-Anweisungen) | 🔲 Spec **`docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md`** |
 
 **Architektur (verbindlich):** Drei Schichten — (1) **Katalog-Dimensionen** (DB, jetzt im Match verdrahtet; **H1:** zusätzlich Prompt-Snippets), (2) **Technik-Disambiguierung** (Code, nur bei `topic_type=technique`), (3) **Didaktik** (Roadmap + LLM-QS, nicht im Vokabular). Progressionsgraph = **Roadmap-first**, **keine Gruppenanalyse**. Bestehender Graph = **leichter Nachfolger-Bias** ab Schritt 2. Trainingsplanung = **eigene Pipeline** (Phase G) — Wiederverwendung der Bausteine, siehe Ist-Doku §16.
 
-**Validierung (Mae Geri, Härtetest):** Pfad-QS vor Optimierung ~65 % → nach Trainer-Roadmap + KI-Gap-Fill **~88 % OK**. Workbench ist **universell** gedacht; Mae Geri war Referenzfall, kein Sonder-Patch.
+**Validierung (Mae Geri, Härtetest):** Roadmap-QS nach Trainer-Roadmap oft **~85–88 %** — gilt **Stufenlogik**, nicht leere Slots. **Gesamt-Pfad-QS** = Minimum aus **`roadmap_qa`** + **`assignment_qa`** (leere Slots → Besetzung ~8–15 %). Workbench universell; Mae Geri Referenzfall.
+
+#### F15 — Match-Dialog, Bewertung, Pfad-QS (Stand 2026-05-22)
+
+| Thema | Ist |
+|--------|-----|
+| **„Übungen matchen“** | Schritt 1: `evaluate_only` (wie „Graph bewerten“) · Schritt 2: `unified_slot_review: true` → Dialog **pro Slot** (Bewertung, Bibliotheks-Alternative, optional KI) |
+| **Vorauswahl Dialog** | Bibliothek nur bei Stufen-Fit **≥ 50 %** und besser als aktuell; bei leerem Slot + schwacher Bibliothek → **KI-Vorschlag** vorausgewählt |
+| **Übernahme** | Nur gewählte Slots speichern — **keine** automatische teure Nach-Bewertung |
+| **Bewertung veraltet** | Nach Graph-Änderungen Hinweis im Findings-Panel; persistiert als **`findings_stale`** im `planning_roadmap`-Artefakt (mit Speichern) |
+| **Getrennte QS** | `path_qa.roadmap_qa` (Stufen/Roadmap/LLM) + `path_qa.assignment_qa` (Slot-Befüllung); **`quality_score`** = Minimum beider |
+| **UX-Fix** | Slot-Karten: stabiler React-Key (`slot-{index}`) — Lernziel editierbar ohne Fokusverlust |
+
+**Code:** `ProgressionOptimizeCompareModal.jsx`, `planning_exercise_path_builder.py` (`_build_unified_slot_review_entry`, `_slot_auto_select_*`), `planning_exercise_path_qa.py` (`build_*_qa_snapshot`), `progression_graph_planning_artifact.py` (`findings_stale`), `progressionGraphDraft.js`
 
 **Backend-Kern:** `planning_progression_roadmap.py`, `planning_exercise_path_builder.py`, `planning_catalog_context.py`, `planning_path_rematch.py`, `planning_path_refine_stage.py`, `planning_path_qa_pipeline.py`, `planning_skill_expectations.py`, `planning_exercise_form_context.py`, `planning_exercise_path_ai_fill.py`, `progression_graph_planning_artifact.py`
 
@@ -129,12 +143,12 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 **Offen (priorisiert):**
 1. Dev-Regression: Gewaltschutz / Breitensport / Kinder (nicht nur Mae Geri)
 2. **PathBuilder-Parität** — gleiche Katalog-Dropdowns wie GraphEditor
-3. QS-UI — positive LLM-Hinweise als Highlights
-4. UI-Wizard (4 Schritte: Ziel → Roadmap → Match → Lücken)
-5. Graph-Erweiterungsmodus (Start ab Knoten)
-6. Phase D′ — Auto KI-Gap-Fill bei persistent leeren Slots
-7. **Trainingsplanung Phase G** — Gruppenkontext-Pack, Scopes `training_section` / `framework_slot` (Ist-Doku §16)
-8. Technik-Katalog konfigurierbar (Backlog)
+3. UI-Wizard (4 Schritte: Ziel → Roadmap → Match → Lücken)
+4. Graph-Erweiterungsmodus (Start ab Knoten)
+5. Phase D′ — Auto KI-Gap-Fill bei persistent leeren Slots
+6. **Trainingsplanung Phase G** — Gruppenkontext-Pack, Scopes `training_section` / `framework_slot` (Ist-Doku §16)
+7. Technik-Katalog konfigurierbar (Backlog)
+8. **H1** — Katalog-Prompt-Snippets (modulare LLM-Anweisungen)
 
 #### Übungs-KI Formular / Schnellanlage (Stand **0.8.171**)
 
@@ -271,8 +285,7 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 
 1. **H1 Katalog-Prompt-Snippets:** modulare LLM-Anweisungen — **`docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md`** (Priorität: Primärfokus → Trainingsstil → Zielgruppe → Stilrichtung).
 2. **Dev-Regression:** Katalog-Match für Gewaltschutz, Breitensport, Kinder — nicht nur Mae-Geri-Härtetest.
-2. **PathBuilder-Parität:** `planning_catalog_context`-Dropdowns auch in `ExerciseProgressionPathBuilder`.
-3. **QS-UI:** positive LLM-Empfehlungen als Highlights statt nur Optimierungspotenziale.
+3. **PathBuilder-Parität:** `planning_catalog_context`-Dropdowns auch in `ExerciseProgressionPathBuilder`.
 4. **UI-Wizard:** 4 Schritte (Ziel & Katalog → Roadmap → Match → Lücken); Backend-Pipeline unverändert.
 5. **Phase D′:** automatisches KI-Gap-Fill bei persistent `roadmap_unfilled`.
 6. **Trainingsplanung G0–G4:** Katalog in Einheits-Editor, Scopes `training_section`/`framework_slot`, Abschnitts-QS, Gruppenkontext-Pack — Details **`PLANNING_PROGRESSION_GRAPH_KI.md`** §16, **`PLANNING_KI_ROADMAP.md`**.

docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md

@@ -1,229 +1,216 @@
-# Planungs-KI — Katalog-Snippets für modulare Prompts
+# Planungs-KI — Katalog-Prompt-Slots (Snippets)
 
 **Stand:** 2026-05-22  
-**Status:** Spezifikation (Phase **H1** — Umsetzung offen)  
-**Bezüge:** `PLANNING_PROGRESSION_GRAPH_KI.md` §4.4 · `AI_PROMPT_TARGET_ARCHITECTURE.md` §2.4 · `planning_catalog_context.py`
+**Status:** **H2** umgesetzt (0.8.235) · **H2.1** Admin-UI + granulare Prompts (0.8.236)  
+**Bezüge:** `PLANNING_PROGRESSION_GRAPH_KI.md` §4.4 · `AI_PROMPT_TARGET_ARCHITECTURE.md` §2.4 · `planning_catalog_context.py` · `catalog_prompt_slots.py`
 
 ---
 
 ## 1. Problem
 
-Seit **F13 (0.8.233)** fließen Primärfokus, Trainingsstil, Zielgruppe und Stilrichtung als `planning_catalog_context` in **Retrieval und Scoring** (`PlanningTargetProfile`).
+Seit **F13 (0.8.233)** fließen Primärfokus, Trainingsstil, Zielgruppe und Stilrichtung als `planning_catalog_context` in **Retrieval und Scoring**.
 
-Die **LLM-Prompts** (Roadmap, Stufen-Spec, Pfad-QS, Gap-Fill) erhalten diese Dimensionen **nicht** als differenzierte Bewertungs- und Planungslogik — höchstens indirekt über Freitext oder JSON-Kataloglisten in Intent-Prompts.
+Die **LLM-Prompts** brauchen zusätzlich **fachliche Textbausteine** pro gewähltem Katalog-Eintrag — editierbar, ohne Code-Deploy, unabhängig von fest codierten Katalog-Namen.
 
-**Folge:** Ein Breitensport-Pfad kann mit Leistungsgruppen-Kriterien bewertet werden; Gewaltschutz wird wie Technik-Curriculum behandelt; QS-Hinweise und Rematch-Vorschläge passen fachlich nicht zum gewählten Kontext.
-
-**Ziel:** Gleiche **Prompt-Basis**, aber **kaskadierte Snippet-Blöcke** pro Katalog-Ausprägung — keine Matrix aus Voll-Prompt-Kopien.
+**Ziel:** Prompts in `ai_prompts` referenzieren **Slot-Typen** (Vokabular). Inhalte hängen an **Katalog-Zeilen** (Stammdaten). Der Resolver füllt Platzhalter zur Laufzeit.
 
 ---
 
-## 2. Priorität der Dimensionen (absteigend)
+## 2. Zwei Ebenen (Kern des Modells)
 
-Verbindliche Reihenfolge bei Konflikten und beim Rollout:
+| Ebene | Was | Wer pflegt | Beispiel |
+|-------|-----|------------|----------|
+| **Slot-Typ** | Art des Textes (festes, erweiterbares Vokabular) | Produkt / Architektur | `hints_on_progression`, `hints_on_path_qa` |
+| **Slot-Wert** | Konkreter Text pro Katalog-Eintrag | Admin / Redakteur | Gewaltschutz → `hints_on_path_qa`: „Lücken = fehlende Deeskalation …“ |
 
-| Rang | Dimension | DB-Tabelle | Snippet-Rolle |
-|------|-----------|------------|----------------|
-| **1** | **Primärfokus** | `focus_areas` | Definiert *worüber* geplant und bewertet wird (Technik-Curriculum vs. Gewaltschutz vs. Fitness …). **Dominant.** |
-| **2** | **Trainingsstil** | `training_types` | Definiert *wie* trainiert wird (Methodik, Belastungsaufbau, Wettkampf vs. Breitenansatz im Training). |
-| **3** | **Zielgruppe** | `target_groups` | Definiert *für wen* (Kinder, Breitensport, Leistungsgruppe) — Tempo, Komplexität, Sicherheit. |
-| **4** | **Stilrichtung** | `style_directions` | Vereins-/Stil-Linie (Shotokan, WKF …) — **Nuancen**, kein neuer Planungstyp. |
-
-**Kaskaden-Regel:** Bei widersprüchlichen Snippet-Aussagen gilt: Primärfokus → Trainingsstil → Zielgruppe → Stilrichtung.
+**Nicht:** feste Attribute pro Eintrag im Code (`planning_lens`, `qa_criteria` …).  
+**Sondern:** beliebig viele Katalog-Einträge × definierte Slot-Typen.
 
 ---
 
-## 3. Architektur — drei Schichten (Erinnerung)
+## 3. Dimensionen & Priorität
 
-| Schicht | Heute | Mit H1 |
-|---------|-------|--------|
-| **Retrieval** | Katalog-Gewichte in `merge_catalog_context_into_target` | unverändert |
-| **Technik-Gates** | `planning_exercise_semantics.py`, nur `topic_type=technique` | unverändert |
-| **LLM-Prompts** | kaum Katalog-spezifisch | **`catalog_guidance_block`** pro Aufruf |
+| Rang | Dimension | `catalog_kind` | DB-Tabelle |
+|------|-----------|----------------|------------|
+| **1** | Primärfokus | `focus_area` | `focus_areas` |
+| **2** | Trainingsstil | `training_type` | `training_types` |
+| **3** | Zielgruppe | `target_group` | `target_groups` |
+| **4** | Stilrichtung | `style_direction` | `style_directions` |
 
-Snippets **ersetzen** keine Technik-Disambiguierung und **duplizieren** keine Retrieval-Gewichte — sie steuern **Didaktik, Bewertungsmaßstäbe und Formulierung** für das Modell.
+**Kaskade:** Bei widersprüchlichen Hinweisen gilt Fokus → Trainingsstil → Zielgruppe → Stilrichtung.
+
+Pro Dimension im Request: **ein aktiver Eintrag** (`is_primary`, sonst höchstes `weight`).
 
 ---
 
-## 4. Snippet-Modell
+## 4. Slot-Typ-Register (Vokabular)
 
-### 4.1 Lookup-Schlüssel
+Definiert in **`catalog_prompt_slot_types`** (DB) + Spiegel in `catalog_prompt_slots.py`.
 
-Pro Katalog-Eintrag ein stabiler **`snippet_key`** (nicht nur numerische ID — IDs können sich in Dev/Import unterscheiden):
+| `slot_key` | Anzeige (DE) | LLM-Prompt | Code-only |
+|------------|--------------|------------|-----------|
+| `description` | Allgemeine Beschreibung | Roadmap, Goal-Analyse, Start/Ziel | — |
+| `hints_on_progression` | Hinweise Progressionsgraph / Stufen | Roadmap, Stage-Spec | — |
+| `hints_on_path_qa` | Bewertungsmaßstäbe Pfad-QS | Path-QA | — |
+| `hints_on_exercise` | Hinweise Übungsanlage / Gap-Fill | Exercise-AI, Suggest (später) | — |
+| `anti_patterns` | Explizit vermeiden (Fehlbewertung) | Path-QA, Stage-Spec | — |
+| `rematch_guard` | Kein Auto-Rematch erzwingen | — | Rematch-Loop (H1.5) |
 
-```
-focus:{slug}          z. B. focus:gewaltschutz
-training_type:{slug}  z. B. training_type:kumite
-target_group:{slug}   z. B. target_group:breitensport
-style:{slug}          z. B. style:shotokan
-```
+**Erweiterung:** neuer Slot-Typ = eine Zeile in `catalog_prompt_slot_types` + Resolver/Admin-Katalog — **kein** neues Python-Feld pro Eintrag.
 
-**Primär** aus `slug` der DB-Zeile; Fallback normalisierter `name` (ASCII, lower, `_`).
+**Fallback `description`:** Wenn kein Slot-Wert gesetzt → `focus_areas.description` (bzw. `description`-Spalte der jeweiligen Katalog-Tabelle).
 
-Mehrfachauswahl im UI: pro Dimension **höchstens ein Snippet** — die Zeile mit `is_primary: true`, sonst erste Zeile, sonst höchste `weight`.
+---
 
-### 4.2 Snippet-Inhalt (Struktur)
+## 5. Platzhalter in `ai_prompts`
 
-Jedes Snippet liefert strukturierte Textbausteine (Deutsch, für LLM):
-
-| Feld | Pflicht | Inhalt |
-|------|---------|--------|
-| `planning_lens` | ja | 2–4 Sätze: Was ist das Planungsziel in dieser Dimension? |
-| `qa_criteria` | ja | Bullet-artige Kriterien für Pfad-QS (was ist „gut“, was ist kein Mangel) |
-| `roadmap_hints` | empfohlen | Stufenlogik, typische Phasen, was vermeiden |
-| `anti_patterns` | optional | Explizite Fehlbewertungen vermeiden (z. B. „keine Wettkampf-Tiefe verlangen“) |
-| `rematch_guard` | optional | Wann **kein** Auto-Rematch sinnvoll (Breitensport: keine Perfektions-Slots erzwingen) |
-
-Phase **H1:** flache Markdown-Strings im Code-Modul.  
-Phase **H2 (optional):** Tabelle `planning_catalog_prompt_snippets` oder JSONB an Katalog-Zeilen, Admin-editierbar.
-
-### 4.3 Platzhalter in `ai_prompts`
-
-Neue **gemeinsame** Platzhalter (Mustache), in alle betroffenen Prompts einfügen:
+Mustache-Keys: **`{catalog_kind}_{slot_key}`** (nur `[a-z0-9_]`).
 
 | Platzhalter | Bedeutung |
 |-------------|-----------|
-| `{{catalog_guidance_block}}` | Gerenderter Gesamttext (alle aktiven Snippets, kaskadiert) |
-| `{{catalog_context_json}}` | Kompakte JSON-Zusammenfassung der gewählten IDs/Namen (Audit) |
-| `{{#has_catalog_guidance}}` … `{{/has_catalog_guidance}}` | Block nur wenn mindestens ein Snippet aktiv |
+| `{{focus_area_description}}` | Aktiver Primärfokus — Beschreibung |
+| `{{focus_area_hints_on_progression}}` | … — Progressions-Hinweise |
+| `{{focus_area_hints_on_path_qa}}` | … — QS-Hinweise |
+| `{{focus_area_hints_on_exercise}}` | … — Übungsanlage |
+| `{{focus_area_anti_patterns}}` | … — Anti-Patterns |
+| `{{training_type_description}}` | Aktiver Trainingsstil — … |
+| `{{training_type_hints_on_progression}}` | … |
+| `{{target_group_hints_on_path_qa}}` | Aktive Zielgruppe — … |
+| `{{style_direction_hints_on_progression}}` | Aktive Stilrichtung — … |
+| *(analog für alle Slot-Typen × Dimension)* | |
+| `{{catalog_guidance_block}}` | **Aggregat** (Abwärtskompatibel): kaskadierter Markdown-Block aus aktiven Slots |
+| `{{catalog_context_json}}` | Audit: IDs, Namen, gesetzte Slot-Keys |
+| `{{has_catalog_guidance}}` | `"true"` oder leer |
 
-**Optional fein (später):** `{{catalog_focus_snippet}}`, `{{catalog_training_type_snippet}}`, … — Phase H1 nur `_block` + JSON.
+**Hinweis:** `prompt_resolver` unterstützt **keine** `{{#if}}`-Blöcke — leere Slots = leerer String.
 
-### 4.4 Betroffene Prompt-Slugs (Reihenfolge Einbindung)
+### 5.1 Prompt-Profile (welche Slots im Aggregat)
 
-| Priorität | Slug | Migration | Wirkung |
-|-----------|------|-----------|---------|
-| 1 | `planning_exercise_path_qa` | bestehend | Pfad-QS, `quality_score`, Empfehlungen |
-| 2 | `planning_progression_roadmap` | 078 | Major Steps, Didaktik |
-| 3 | `planning_progression_stage_spec` | 079 | Stufen-Gates, Erfolgskriterien |
-| 4 | `planning_progression_start_target` | 087 | Start/Ziel-Extraktion |
-| 5 | `planning_progression_goal_analysis` | 078 | Zielanalyse |
-| 6 | Gap-Fill / Übungs-KI | 085+ | `planning_context` ergänzen |
+| Prompt-Slug | Aggregat enthält primär |
+|-------------|-------------------------|
+| `planning_exercise_path_qa` | `*_hints_on_path_qa`, `*_anti_patterns`, `*_description` |
+| `planning_progression_roadmap` | `*_description`, `*_hints_on_progression`, `*_anti_patterns` |
+| `planning_progression_stage_spec` | `*_hints_on_progression`, `*_anti_patterns` |
+| `planning_progression_goal_analysis` | `*_description` |
 
-Intent-Prompts (`planning_exercise_search_intent`, …) **optional** Phase H1.5 — dort bereits Katalog-JSON.
+Feinsteuerung: im Admin-Template **granulare** Platzhalter nutzen; Aggregat optional.
 
 ---
 
-## 5. Builder (Backend)
+## 6. Speicherung (DB)
 
-**Neues Modul:** `backend/planning_catalog_prompt_snippets.py`
+### 6.1 `catalog_prompt_slot_types`
 
-```python
-def build_catalog_guidance_for_prompt(
-    cur,
-    catalog: Optional[ProgressionPlanningCatalogContext],
-) -> Dict[str, str]:
-    """
-    Returns:
-      catalog_guidance_block: str
-      catalog_context_json: str
-      has_catalog_guidance: bool
-      snippet_keys: list[str]   # Metadaten für Logs/Tests
-    """
+Metadaten je Slot-Typ (`slot_key`, `display_name`, `description`, `applicable_kinds[]`, `sort_order`, `for_llm`, `for_code`).
+
+### 6.2 `catalog_prompt_slots`
+
+```text
+catalog_kind   — focus_area | training_type | target_group | style_direction
+catalog_id     — FK auf jeweilige Stammtabelle (logisch, kein polymorpher FK)
+slot_key       — FK → catalog_prompt_slot_types
+content        — TEXT (Markdown/Plain für LLM)
+UNIQUE (catalog_kind, catalog_id, slot_key)
 ```
 
-**Ablauf:**
-
-1. `catalog` aus Request oder `planning_roadmap.planning_catalog_context` (wie F13).
-2. Pro Dimension aktives Item auflösen → `snippet_key` → Text aus Registry.
-3. Snippets in **Prioritätsreihenfolge** §2 zu `_block` zusammenfügen (Überschriften: „Primärfokus“, „Trainingsstil“, …).
-4. Fehlende Snippets: Dimension **weglassen** (kein Default-Text) — besser kein Snippet als falscher.
-
-**Einbindung:** Orchestratoren rufen Builder auf und mergen in `variables` vor `load_and_render_ai_prompt`:
-
-- `planning_exercise_path_qa.py` → `try_llm_qa_progression_path`
-- `planning_progression_roadmap.py` (Roadmap-/Stage-Pipeline)
-- `planning_exercise_path_builder.py` (catalog an QA/Match durchreichen)
-
-`ProgressionPathSuggestRequest` trägt `planning_catalog_context` bereits — kein neues API-Feld nötig.
+Neuer Katalog-Eintrag im Admin → **keine** Code-Änderung; Slots optional befüllen.
 
 ---
 
-## 6. Beispiel-Snippets (Review-Entwurf)
+## 7. Laufzeit-Architektur
 
-### 6.1 Primärfokus — Gewaltschutz (`focus:gewaltschutz`)
+```text
+planning_catalog_context (Request / Graph-Artefakt)
+        ↓
+catalog_prompt_slots.resolve_catalog_prompt_variables(cur, catalog, slug?)
+        ↓
+planning_prompt_variables.merge_planning_prompt_variables(...)
+        ↓
+load_and_render_ai_prompt (ai_prompts Template)
+```
 
-**planning_lens:** Planung zielt auf Prävention, Deeskalation, Grenzen und sichere Übungsformen — nicht auf Wettkampf-Perfektion oder Technik-Show.
+**Module:**
 
-**qa_criteria:** Gute Pfade bauen Sicherheit, Kommunikation und Alternativen auf; „Lücken“ sind fehlende Deeskalations- oder Rollenspiel-Stufen, nicht fehlende Kick-Varianten.
-
-**anti_patterns:** Nicht nach Kumite-Tiefe, Explosivität oder Wettkampf-Belastung bewerten.
-
-### 6.2 Primärfokus — Technik / Kumite-Beinarbeit (`focus:kumite` o. ä.)
-
-**planning_lens:** Curriculum für eine Technik oder Kumite-Teilaspekt; aufeinander aufbauende Belastung und Anwendungsnähe sind erwünscht.
-
-**qa_criteria:** Kohärente Progression Grundlagen → Anwendung → Vertiefung; Übergänge ohne Sprünge; themenfremde Kraft-/Ausdauer-Inseln abwerten.
-
-### 6.3 Trainingsstil — Breitensport (`training_type:breitensport` o. Name-Match)
-
-**planning_lens:** Partizipation, Verständlichkeit, Freude am Bewegen; weniger maximale Spezialisierung.
-
-**qa_criteria:** Hohe OK-Rate bei moderatem Schwierigkeitsanstieg; „Perfektion“-Stufen nur optional, nicht als Pflicht-Lücke.
-
-**rematch_guard:** Keine leeren Slots erzwingen, nur um eine Leistungs-Perfektionsstufe zu füllen.
-
-### 6.4 Zielgruppe — Leistungsgruppe (`target_group:leistungsgruppe`)
-
-**qa_criteria:** Höhere Anspruchskurven, Belastungs- und Kombinationsprogressionen sind relevant; Lücken in Spezialisierung können echte Hinweise sein.
-
-*(Weitere Snippets iterativ ergänzen — nicht alle Katalog-Zeilen sofort.)*
+| Modul | Rolle |
+|-------|--------|
+| `catalog_prompt_slots.py` | Slot-Typen, DB-Lese/Schreib, Resolver, Aggregat-Block |
+| `planning_prompt_variables.py` | Zentrale Provider-Liste (erweiterbar) |
+| `planning_catalog_prompt_snippets.py` | Deprecated Re-Exports (Tests/Kompatibilität) |
 
 ---
 
-## 7. Rollout-Phasen
+## 8. Admin-API
 
-### H1 — Minimal viable (Progressionsgraph)
+| Methode | Pfad | Beschreibung |
+|---------|------|--------------|
+| GET | `/api/catalog-prompt-slot-types` | Slot-Typ-Register |
+| GET | `/api/catalog-prompt-slots/{kind}/{catalog_id}` | Alle Slots eines Eintrags |
+| PUT | `/api/catalog-prompt-slots/{kind}/{catalog_id}` | Slots upserten (Admin) |
 
-- [ ] Modul `planning_catalog_prompt_snippets.py` + Registry (5–8 Snippets: 2–3 Foki, 2 Trainingsstile, 2 Zielgruppen)
-- [ ] Einbindung in **`planning_exercise_path_qa`** + **`planning_progression_roadmap`** + **`planning_progression_stage_spec`**
-- [ ] Migration Prompt-Templates: Abschnitt `{{#has_catalog_guidance}}…{{/has_catalog_guidance}}`
-- [ ] Tests: gleicher Pfad + unterschiedlicher Katalog → unterschiedlicher `catalog_guidance_block`; Snapshot QA-Variablen
-- [ ] Dev-Regression: Gewaltschutz, Breitensport, Kinder — **Hinweistexte** müssen zum Kontext passen (nicht Mae-Geri-Kriterien)
+`kind`: `focus_area` · `training_type` · `target_group` · `style_direction`
+
+**Später:** UI-Tabs am Katalog-Editor; Versionierung/Audit wie `ai_prompts`.
+
+---
+
+## 9. Rollout-Phasen
+
+### H1 — Bootstrap (0.8.234) ✓
+
+Hardcodierte `SNIPPET_REGISTRY` — Proof of Concept für `catalog_guidance_block`.
+
+### H2 — Slot-Modell (0.8.235) ✓
+
+- [x] Tabellen `catalog_prompt_slot_types`, `catalog_prompt_slots`
+- [x] Seed aus H1-Texten (Name-Match auf Stammdaten)
+- [x] Resolver mit granularen Platzhaltern + Aggregat
+- [x] Admin-API GET/PUT
+- [x] `SNIPPET_REGISTRY` aus Laufzeit-Pfad entfernt
+
+### H2.1 — Admin-UI
+
+- [x] Slot-Editor an Fokusbereich / Trainingsstil / Zielgruppe / Stilrichtung (`CatalogPromptSlotsEditor`, Stammdaten-Katalog)
+- [x] Prompt-Templates mit granularen Platzhaltern (Migration 093)
+- [ ] Platzhalter-Hilfe im KI-Prompt-Editor (erweitert)
 
 ### H1.5
 
-- [ ] Rematch/Refine: `rematch_guard` aus Snippets respektieren (weniger Ping-Pong bei Breitensport)
-- [ ] Intent-Prompts + Gap-Fill-Kontext
+- [ ] `rematch_guard` im Rematch-Loop
+- [ ] Intent-Prompts + Gap-Fill: `hints_on_exercise`
 
-### H2 — Betrieb
+### H3 — Trainingsplanung (Phase G)
 
-- [ ] Snippets in DB, Admin-UI oder Markdown-Import
-- [ ] Versionierung / Audit wie `ai_prompts`
-
-### H3 — Phase G (Trainingsplanung)
-
-- [ ] Gleicher Builder, anderer Orchestrator (Abschnitts-QS, Slot-Suggest)
+- [ ] Gleicher Resolver, andere Orchestratoren
 
 ---
 
-## 8. Tests & Akzeptanz
+## 10. Tests & Akzeptanz
 
 | Test | Erwartung |
 |------|-----------|
-| `test_catalog_prompt_snippets_priority` | Bei Konflikt gewinnt Fokus-Snippet-Text in `_block`-Reihenfolge |
-| `test_path_qa_variables_include_guidance` | Mit Gewaltschutz-Kontext enthält gerendeter Prompt „Deeskalation“ o. ä., nicht „Kumite-Perfektion“ |
-| `test_path_qa_no_snippet_without_catalog` | Ohne Katalog: `has_catalog_guidance=false`, Prompt unverändert wie heute |
-| Manuell | Mae-Geri-Pfad + Breitensport-Kontext: QS-Highlights ohne Leistungs-Belastungs-Forderungen |
-
-**Nicht Ziel von H1:** Retrieval-Gewichte neu kalibrieren; Technik-Tuples externalisieren (separates Backlog **H** in Roadmap).
+| Slot aus DB | Gewaltschutz + `hints_on_path_qa` → Platzhalter enthält Deeskalation |
+| Ohne Katalog | Alle `{{focus_area_*}}` leer; `has_catalog_guidance` leer |
+| Neuer Eintrag | Leere Slots, kein Crash; `description`-Fallback aus Stammdaten |
+| Priorität | Aggregat: Primärfokus vor Trainingsstil |
 
 ---
 
-## 9. Abgrenzung zu anderen Fixes
+## 11. Abgrenzung
 
-| Thema | Dokument / Fix |
-|-------|----------------|
-| 88 % vs. 65 % falsche Pipeline | Evaluate-only für Pfad-QS; fairer Compare — Code-Stand 2026-05-22 |
-| Triviale ID-Tausche im Dialog | `_filter_trivial_slot_diffs` |
-| Katalog nur im Retrieval | F13 — bleibt, Snippets ergänzen LLM-Schicht |
-
-Snippets lösen **fachliche Fehlbewertung** — nicht Pipeline-Inkonsistenz allein.
+| Thema | Hinweis |
+|-------|---------|
+| Retrieval-Gewichte | `merge_catalog_context_into_target` — unverändert |
+| Technik-Gates | `planning_exercise_semantics` — unverändert |
+| Prompt-Text | `ai_prompts` — editierbar, referenziert Slot-Platzhalter |
 
 ---
 
-## 10. Changelog
+## 12. Changelog
 
 | Datum | Änderung |
 |-------|----------|
-| 2026-05-22 | Erstfassung — Priorität Primärfokus → Trainingsstil → Zielgruppe → Stilrichtung; H1–H3 Rollout |
+| 2026-05-22 | **H2.1** (0.8.236): Admin-UI `CatalogPromptSlotsEditor`; Migration 093 granulare Prompt-Templates |
+| 2026-05-22 | **H2** (0.8.235): Slot-Typ-Register + `catalog_prompt_slots` DB, granulare Platzhalter, Admin-API |
+| 2026-05-22 | Konzept §4–§8: zwei Ebenen Slot-Typ vs. Slot-Wert; Platzhalter `{kind}_{slot_key}` |
+| 2026-05-22 | H1 (0.8.234): Bootstrap-Registry — durch H2 ersetzt |
+| 2026-05-22 | Erstfassung |

docs/architecture/PLANNING_KI_ROADMAP.md

@@ -89,14 +89,24 @@ Details und Module: **`PLANNING_PROGRESSION_GRAPH_KI.md`**.
 - [x] Vier Planungskontext-Dropdowns im Editor
 - [x] `progressionGraphDraft.js` — Artefakt + API-Payload
 
+### F15 — Match-Dialog & getrennte Pfad-QS (2026-05-22, lokal)
+
+- [x] **`unified_slot_review`** — Dialog pro Slot (Bibliothek + KI, Stufen-Fit-Vergleich)
+- [x] Vorauswahl: Bibliothek nur bei Stufen-Fit ≥ 50 %; sonst KI bei leerem/schwachem Slot
+- [x] Übernahme ohne teure Auto-Nach-Bewertung; manuell „Graph bewerten“
+- [x] **`path_qa.roadmap_qa`** + **`path_qa.assignment_qa`**; Gesamt = Minimum
+- [x] **`findings_stale`** im Graph-Artefakt — Hinweis „Bewertung veraltet“ (persistiert)
+- [x] Slot-Key-Fix — Lernziel editierbar ohne Fokusverlust
+
 ### Validierung (Referenz Mae Geri, 2026-05)
 
-| Phase | Pfad-QS | Ergebnis |
-|-------|---------|----------|
-| Vor Roadmap/KI | ~65 % | Lücken, falsche Reihenfolge, Off-Topic |
-| Nach Trainer-Roadmap + KI-Gap-Fill | **~88 % OK** | Vollständige Abdeckung; positive LLM-Hinweise |
+| Phase | Roadmap-QS | Besetzung | Gesamt | Ergebnis |
+|-------|------------|-----------|--------|----------|
+| Vor Roadmap/KI | — | — | ~65 % | Lücken, Off-Topic |
+| Roadmap ok, Slots leer | ~88 % | ~8–15 % | **~8–15 %** | Besetzung fehlt |
+| Nach Match + Fill | ~88 % | hoch | **~85 %+** | Vollständige Abdeckung |
 
-**Fazit:** Workbench + Katalog + Roadmap sind universell; Technik-Hardcoding allein reicht für Didaktik nicht.
+**Fazit:** Roadmap-QS und Besetzungs-QS getrennt betrachten; Workbench + Katalog + Roadmap universell.
 
 ---
 

docs/architecture/PLANNING_PROGRESSION_GRAPH_KI.md

@@ -157,6 +157,10 @@ flowchart TB
 | `auto_refine_stage_spec` | bool | Stufen-Spec bei `stage_mismatch` schärfen (Default **true**) |
 | `max_rematch_rounds` | int | Rematch-Runden 0–4 (Default **3**) |
 | `include_path_qa`, `include_llm_path_qa`, `include_ai_gap_fill` | bool | QS, LLM-Ganzpfad, Lücken-Angebote |
+| `evaluate_only` | bool | Nur QS auf `evaluate_steps[]` — kein Match |
+| `unified_slot_review` | bool | Pro-Slot-Review (Bibliothek + optional KI) für Match-Dialog; erfordert `baseline_evaluate_steps` + Roadmap |
+| `baseline_evaluate_steps` | array? | Slot-Stand für Schritt 1 / Review-Baseline |
+| `baseline_path_qa_snapshot` | object? | `path_qa` aus evaluate_only (Schritt 1 des Match-Flows) |
 
 ### 4.2 Wichtige Response-Felder
 
@@ -166,7 +170,9 @@ flowchart TB
 | `steps[]` | Gematchte Übungen; pro Schritt u. a. `roadmap_*`, `skill_expectations` |
 | `path_skill_expectations` | Pfadweite Skill-Erwartungen |
 | `gap_fill_offers[]` | Lücken mit `context_preview`, `goal_for_ai` |
-| `path_qa` | QS inkl. `qa_tiers`, `optimization_hints`, `rematch_log`, `refine_log` |
+| `path_qa` | QS inkl. `qa_tiers`, `optimization_hints`, `rematch_log`, `refine_log`; **F15:** auch `roadmap_qa`, `assignment_qa` (siehe §8.1) |
+| `slot_reviews[]` | Bei `unified_slot_review`: je Slot `library_alternative`, `ai_alternative`, `auto_select`-Flags |
+| `findings_stale` | Im Graph-Artefakt (nicht API-Response): Bewertung veraltet seit letztem „Graph bewerten“ |
 | `target_profile_summary` | Erwartungsprofil inkl. Katalog-Dimensionen (nach Match) |
 | `match_summary` | `library_matches`, `gap_fill_offer_count`, `roadmap_unfilled_count` |
 
@@ -221,12 +227,13 @@ Tests: `test_planning_roadmap_stage_match.py`, `test_planning_path_rematch.py`,
 
 ### Referenz-Validierung (Mae Geri, 2026-05)
 
-| Phase | Pfad-QS | Ergebnis |
-|-------|---------|----------|
-| Vor Roadmap/KI-Anpassung | ~65 % | Strukturelle Lücken (Grundlagen, Reihenfolge, Zielgenauigkeit) |
-| Nach Trainer-Roadmap + KI-Angebote in leeren Slots | **~88 % OK** | Vollständige Curriculum-Abdeckung; positive LLM-Empfehlungen |
+| Phase | Roadmap-QS | Besetzung | Gesamt (min) | Ergebnis |
+|-------|------------|-----------|--------------|----------|
+| Vor Roadmap/KI-Anpassung | — | — | ~65 % | Strukturelle Lücken |
+| Nach Trainer-Roadmap, **Slots leer** | ~85–88 % | ~8–15 % | **~8–15 %** | Roadmap ok, Besetzung fehlt |
+| Nach Match + befüllte Slots | ~85–88 % | hoch | **~85 %+** | Vollständige Curriculum-Abdeckung |
 
-**Lesson:** Workbench + Katalog-Kontext + Roadmap sind der Hebel; Technik-Hardcoding allein reicht nicht für Didaktik.
+**Lesson:** **`roadmap_qa`** und **`assignment_qa`** getrennt interpretieren; Gesamt-QS allein bei leerer Roadmap irreführend (historisch nur LLM-Roadmap-Score).
 
 ---
 
@@ -330,6 +337,21 @@ API: `path_qa.qa_tiers`, `path_qa.optimization_hints` — **kein** anfrage-spezi
 
 Phase G: gleiche Tiers für Abschnitts-QS nach Einheits-Match.
 
+### 8.1 Getrennte Pfad-QS — Roadmap vs. Übungsbesetzung (F15)
+
+`build_path_qa_summary()` in `planning_exercise_path_qa.py` liefert drei Ebenen:
+
+| Feld | Inhalt | Score-Logik |
+|------|--------|-------------|
+| **`roadmap_qa`** | Stufenlogik, LLM `topic_coverage`, Roadmap-Hinweise | LLM-`quality_score` oder heuristisch (Lücken, Hints) |
+| **`assignment_qa`** | Leere Slots, Off-Topic auf belegten Slots, Fill-Statistik | Stark abwertend bei leeren Slots (~8–15 % bei 100 % leer) |
+| **`quality_score`** (gesamt) | Anzeige „Pfad-QS gesamt“ | **`min(roadmap_qa, assignment_qa)`** |
+| **`overall_ok`** | Gesamt-OK | Beide Dimensionen müssen OK sein |
+
+UI: **`ProgressionFindingsPanel`** — zwei Unterblöcke; Match-Dialog zeigt Roadmap- vs. Besetzungs-Prozent. Nach Graph-Änderung: **`findings_stale: true`** im Artefakt → Hinweis „Bewertung veraltet“ (bis erneut „Graph bewerten“ + Speichern).
+
+Tests: `test_planning_path_qa_split.py`, `test_planning_deterministic_quality_score.py`
+
 ## 9. Fähigkeiten-Scoring-Anbindung
 
 Modul: `planning_skill_expectations.py`
@@ -379,6 +401,7 @@ Kontext-Helfer: `frontend/src/utils/planningContextForExerciseAi.js`
 | **F12** | Post-Match-Gate, LLM-QA nach Rematch, Gap-Timing, `roadmap_unfilled`-Sync | ✅ | 0.8.231–0.8.232 |
 | **F13** | **Katalog-Kontext** (`planning_catalog_context`) im Match + Graph-Artefakt | ✅ | **0.8.233** |
 | **F14** | `ProgressionGraphEditor` Slot-UI + Planungskontext-Dropdowns | ✅ | 0.8.233 |
+| **F15** | Unified Slot-Review, getrennte Pfad-QS, `findings_stale`, Match-Vorauswahl | ✅ | lokal (2026-05-22) |
 | **H1** | **Katalog-Prompt-Snippets** — modulare LLM-Anweisungen pro Dimension | 🔲 | Spec **`PLANNING_CATALOG_PROMPT_SNIPPETS.md`** |
 | **G** | Trainingsplanung: eigene Pipeline + Wiederverwendung Bausteine (§16) | 🔲 | — |
 | **UX** | Wizard/Stepper; PathBuilder-Parität Katalog | 🔲 | — |
@@ -391,8 +414,7 @@ Kontext-Helfer: `frontend/src/utils/planningContextForExerciseAi.js`
 
 1. **H1 Katalog-Prompt-Snippets** — modulare LLM-Anweisungen (Priorität: Primärfokus → Trainingsstil → Zielgruppe → Stilrichtung) — **`PLANNING_CATALOG_PROMPT_SNIPPETS.md`**
 2. **Dev-Regression:** Gewaltschutz + Breitensport + Kinder (ohne Mae Geri) — Katalog-Match verifizieren
-2. **PathBuilder-Parität** — gleiche `planning_catalog_context`-Dropdowns in `ExerciseProgressionPathBuilder`
-3. **QS-UI** — positive LLM-Hinweise als „Highlights“, nicht als „Optimierungspotenziale“
+3. **PathBuilder-Parität** — gleiche `planning_catalog_context`-Dropdowns in `ExerciseProgressionPathBuilder`
 4. **UI-Wizard** — 4 Schritte (Ziel → Roadmap → Match → Lücken); Backend unverändert
 5. **Graph-Erweiterungsmodus** — Start ab gewähltem Knoten / letzter Sequenz
 6. **Phase D′** — automatisches KI-Gap-Fill bei persistent leeren Slots

frontend/src/components/ExerciseProgressionGraphPanel.jsx

@@ -13,7 +13,7 @@ import { Link, useLocation } from 'react-router-dom'
 import api from '../utils/api'
 import SkillProfilePanel from './skills/SkillProfilePanel'
 import { useAuth } from '../context/AuthContext'
-import { activeClubMemberships, getTenantClubDependencyKey } from '../utils/activeClub'
+import { activeClubMemberships, getDefaultClubIdForGovernanceForms, getTenantClubDependencyKey } from '../utils/activeClub'
 import ProgressionGraphEditor from './ProgressionGraphEditor'
 import ProgressionGraphListCard from './ProgressionGraphListCard'
 import { EXERCISE_VISIBILITY_FIELD_LABEL } from '../constants/exerciseGovernanceLabels'
@@ -24,6 +24,21 @@ const VIS_OPTIONS = [
   { value: 'official', label: 'Offiziell' },
 ]
 
+const GRAPH_VISIBILITY_PROMOTION_LABEL = {
+  club: 'Vereins-Sichtbarkeit',
+  official: 'offizielle Sichtbarkeit',
+}
+
+/** Graph-Promotion mit optionalem Übungs-Anheben (private→club/official, club→official). */
+function shouldPromptGraphExercisePromotion(prevVis, nextVis) {
+  const p = (prevVis || 'private').trim().toLowerCase()
+  const n = (nextVis || 'private').trim().toLowerCase()
+  return (
+    (p === 'private' && (n === 'club' || n === 'official')) ||
+    (p === 'club' && n === 'official')
+  )
+}
+
 function edgeTypeLabel(type) {
   if (type === 'next_exercise') return 'Nachfolger'
   if (type === 'sibling') return 'Schwester'
@@ -41,7 +56,9 @@ function ExerciseProgressionGraphPanel(
   const { user } = useAuth()
   const location = useLocation()
   const isSuperadmin = user?.role === 'superadmin'
+  const memberClubs = useMemo(() => activeClubMemberships(user?.clubs), [user?.clubs])
   const tenantClubDepKey = useMemo(() => getTenantClubDependencyKey(user), [user])
+  const [clubsForGovernanceForms, setClubsForGovernanceForms] = useState([])
 
   const filteredGraphVisOptions = useMemo(
     () => VIS_OPTIONS.filter((o) => o.value !== 'official' || isSuperadmin),
@@ -61,6 +78,37 @@ function ExerciseProgressionGraphPanel(
   const [metaName, setMetaName] = useState('')
   const [metaDescription, setMetaDescription] = useState('')
   const [metaVisibility, setMetaVisibility] = useState('private')
+  const [metaClubSelect, setMetaClubSelect] = useState('')
+
+  const memberClubIdSet = useMemo(
+    () => new Set(memberClubs.map((c) => Number(c.id))),
+    [memberClubs],
+  )
+
+  const sortedMemberClubs = useMemo(
+    () =>
+      [...memberClubs].sort((a, b) =>
+        String(a.name || '').localeCompare(String(b.name || ''), 'de'),
+      ),
+    [memberClubs],
+  )
+
+  const sortedOtherGovernanceClubs = useMemo(() => {
+    if (!isSuperadmin || clubsForGovernanceForms.length === 0) return []
+    return clubsForGovernanceForms
+      .filter((c) => !memberClubIdSet.has(Number(c.id)))
+      .sort((a, b) => String(a.name || '').localeCompare(String(b.name || ''), 'de'))
+  }, [isSuperadmin, clubsForGovernanceForms, memberClubIdSet])
+
+  const showGovernanceClubOptgroups =
+    isSuperadmin && sortedMemberClubs.length > 0 && sortedOtherGovernanceClubs.length > 0
+
+  const governanceClubSelectOptions = useMemo(() => {
+    if (isSuperadmin && clubsForGovernanceForms.length > 0) {
+      return [...sortedMemberClubs, ...sortedOtherGovernanceClubs]
+    }
+    return sortedMemberClubs
+  }, [isSuperadmin, clubsForGovernanceForms.length, sortedMemberClubs, sortedOtherGovernanceClubs])
 
   const [filterAnchorOnly, setFilterAnchorOnly] = useState(!!anchorExerciseId)
   const [editingEdgeNotes, setEditingEdgeNotes] = useState(null)
@@ -125,6 +173,25 @@ function ExerciseProgressionGraphPanel(
     }
   }, [refreshGraphs, tenantClubDepKey])
 
+  useEffect(() => {
+    if (!isSuperadmin) {
+      setClubsForGovernanceForms([])
+      return undefined
+    }
+    let cancelled = false
+    ;(async () => {
+      try {
+        const list = await api.listClubs()
+        if (!cancelled) setClubsForGovernanceForms(Array.isArray(list) ? list : [])
+      } catch {
+        if (!cancelled) setClubsForGovernanceForms([])
+      }
+    })()
+    return () => {
+      cancelled = true
+    }
+  }, [isSuperadmin, tenantClubDepKey])
+
   useEffect(() => {
     if (!selectedGraphId) {
       setSkillProfileData(null)
@@ -157,6 +224,7 @@ function ExerciseProgressionGraphPanel(
       setMetaName('')
       setMetaDescription('')
       setMetaVisibility('private')
+      setMetaClubSelect('')
       return
     }
     const g = graphs.find((x) => x.id === selectedGraphId)
@@ -164,6 +232,12 @@ function ExerciseProgressionGraphPanel(
       setMetaName(g.name || '')
       setMetaDescription(g.description || '')
       setMetaVisibility(g.visibility || 'private')
+      if (g.club_id != null) {
+        setMetaClubSelect(String(g.club_id))
+      } else {
+        const fallback = getDefaultClubIdForGovernanceForms(user)
+        setMetaClubSelect(fallback != null ? String(fallback) : '')
+      }
     }
     let cancelled = false
     ;(async () => {
@@ -176,7 +250,17 @@ function ExerciseProgressionGraphPanel(
     return () => {
       cancelled = true
     }
-  }, [selectedGraphId, graphs, refreshEdges])
+  }, [selectedGraphId, graphs, refreshEdges, user])
+
+  const resolveGovernanceClubId = useCallback(() => {
+    const g = graphs.find((x) => x.id === selectedGraphId)
+    if (g?.club_id != null) return Number(g.club_id)
+
+    const sel = String(metaClubSelect || '').trim()
+    if (sel && /^\d+$/.test(sel)) return Number(sel)
+
+    return getDefaultClubIdForGovernanceForms(user)
+  }, [graphs, selectedGraphId, metaClubSelect, user])
 
   const filteredEdges = useMemo(() => {
     if (!filterAnchorOnly || anchorExerciseId == null) return edges
@@ -226,13 +310,7 @@ function ExerciseProgressionGraphPanel(
     }
   }
 
-  const resolvePromoteClubId = () => {
-    const g = graphs.find((x) => x.id === selectedGraphId)
-    if (g?.club_id != null) return Number(g.club_id)
-    const memberships = activeClubMemberships(user?.clubs)
-    const active = memberships.find((c) => c.is_active) || memberships[0]
-    return active?.club_id != null ? Number(active.club_id) : null
-  }
+  const resolvePromoteClubId = resolveGovernanceClubId
 
   const handleSaveMeta = async () => {
     if (!selectedGraphId) return
@@ -247,35 +325,39 @@ function ExerciseProgressionGraphPanel(
 
     setBusy(true)
     try {
-      if (prevVis === 'private' && nextVis === 'club') {
+      if (shouldPromptGraphExercisePromotion(prevVis, nextVis)) {
         const preview = await api.getProgressionGraphVisibilityPromotionCandidates(
           selectedGraphId,
-          { targetVisibility: 'club' },
+          { targetVisibility: nextVis },
         )
-        const privateExercises = Array.isArray(preview?.exercises) ? preview.exercises : []
-        if (privateExercises.length > 0) {
-          const titles = privateExercises
+        const promotionExercises = Array.isArray(preview?.exercises) ? preview.exercises : []
+        if (promotionExercises.length > 0) {
+          const visLabel = GRAPH_VISIBILITY_PROMOTION_LABEL[nextVis] || nextVis
+          const titles = promotionExercises
             .slice(0, 8)
             .map((ex) => `• ${ex.title || `Übung #${ex.id}`}`)
             .join('\n')
           const more =
-            privateExercises.length > 8
-              ? `\n… und ${privateExercises.length - 8} weitere`
+            promotionExercises.length > 8
+              ? `\n… und ${promotionExercises.length - 8} weitere`
               : ''
           const promote = window.confirm(
-            `Der Graph wird auf „Verein“ gestellt. Im Graph sind noch ${privateExercises.length} private Übung(en):\n\n${titles}${more}\n\nDiese Übungen ebenfalls auf Vereins-Sichtbarkeit anheben?`,
+            `Der Graph wird auf „${visLabel}“ gestellt. Im Graph sind noch ${promotionExercises.length} Übung(en) mit niedrigerer Sichtbarkeit:\n\n${titles}${more}\n\nDiese Übungen ebenfalls auf ${visLabel} anheben?`,
           )
           if (promote) {
-            const clubId = resolvePromoteClubId()
+            let clubId = null
+            if (nextVis === 'club') {
+              clubId = resolvePromoteClubId()
               if (!clubId) {
-              alert('Kein aktiver Verein — Übungen können nicht auf Verein promoted werden.')
-            } else {
-              const ids = privateExercises.map((ex) => ex.id).filter((id) => id != null)
-              const res = await api.bulkPatchExercisesMetadata({
-                exercise_ids: ids,
-                visibility: 'club',
-                club_id: clubId,
-              })
+                throw new Error(
+                  'Kein Verein gewählt — bitte unter „Verein zuordnen“ einen Verein auswählen oder den Vereins-Umschalter nutzen.',
+                )
+              }
+            }
+            const ids = promotionExercises.map((ex) => ex.id).filter((id) => id != null)
+            const bulkPayload = { exercise_ids: ids, visibility: nextVis }
+            if (nextVis === 'club' && clubId != null) bulkPayload.club_id = clubId
+            const res = await api.bulkPatchExercisesMetadata(bulkPayload)
             if ((res?.failed || []).length) {
               const f = res.failed[0]
               throw new Error(f?.detail || 'Übungs-Promotion fehlgeschlagen')
@@ -283,12 +365,18 @@ function ExerciseProgressionGraphPanel(
           }
         }
       }
-      }
 
+      const promoteClubId = nextVis === 'club' ? resolvePromoteClubId() : null
+      if (nextVis === 'club' && !promoteClubId) {
+        throw new Error(
+          'Vereins-Sichtbarkeit: Bitte einen Verein unter „Verein zuordnen“ wählen oder den Vereins-Umschalter setzen.',
+        )
+      }
       await api.updateExerciseProgressionGraph(selectedGraphId, {
         name,
         description: metaDescription.trim() || null,
         visibility: metaVisibility,
+        ...(promoteClubId != null ? { club_id: promoteClubId } : {}),
       })
       await refreshGraphs()
       alert('Graph-Metadaten gespeichert.')
@@ -539,7 +627,14 @@ function ExerciseProgressionGraphPanel(
             <select
               className="form-input"
               value={metaVisibility}
-              onChange={(e) => setMetaVisibility(e.target.value)}
+              onChange={(e) => {
+                const v = e.target.value
+                setMetaVisibility(v)
+                if (v === 'club' && !metaClubSelect) {
+                  const fb = getDefaultClubIdForGovernanceForms(user)
+                  if (fb != null) setMetaClubSelect(String(fb))
+                }
+              }}
             >
               {filteredGraphVisOptions.map((o) => (
                 <option key={o.value} value={o.value}>
@@ -548,6 +643,42 @@ function ExerciseProgressionGraphPanel(
               ))}
             </select>
           </div>
+          {metaVisibility === 'club' ? (
+            <div className="form-row">
+              <label className="form-label">Verein zuordnen</label>
+              <select
+                className="form-input"
+                value={metaClubSelect}
+                onChange={(e) => setMetaClubSelect(e.target.value)}
+              >
+                <option value="">Aktiver Verein (Vereins-Umschalter / Header)</option>
+                {showGovernanceClubOptgroups ? (
+                  <>
+                    <optgroup label="Meine Vereine">
+                      {sortedMemberClubs.map((c) => (
+                        <option key={c.id} value={String(c.id)}>
+                          {c.name || `Verein #${c.id}`}
+                        </option>
+                      ))}
+                    </optgroup>
+                    <optgroup label="Weitere Vereine">
+                      {sortedOtherGovernanceClubs.map((c) => (
+                        <option key={c.id} value={String(c.id)}>
+                          {c.name || `Verein #${c.id}`}
+                        </option>
+                      ))}
+                    </optgroup>
+                  </>
+                ) : (
+                  governanceClubSelectOptions.map((c) => (
+                    <option key={c.id} value={String(c.id)}>
+                      {c.name || `Verein #${c.id}`}
+                    </option>
+                  ))
+                )}
+              </select>
+            </div>
+          ) : null}
           <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap' }}>
             <button type="button" className="btn btn-secondary" disabled={busy} onClick={handleSaveMeta}>
               Metadaten speichern

frontend/src/components/ExerciseProgressionPathBuilder.jsx

@@ -589,6 +589,7 @@ export default function ExerciseProgressionPathBuilder({
   const [gapPrepFocusAreaId, setGapPrepFocusAreaId] = useState('')
   const [gapPrepError, setGapPrepError] = useState('')
   const [loadedPlanningHint, setLoadedPlanningHint] = useState(false)
+  const [graphGovernance, setGraphGovernance] = useState({ visibility: 'private', clubId: null })
   const [wizardStep, setWizardStep] = useState(1)
   const [pathInsertNotice, setPathInsertNotice] = useState('')
 
@@ -670,6 +671,10 @@ export default function ExerciseProgressionPathBuilder({
       .getExerciseProgressionGraph(Number(graphId))
       .then((g) => {
         if (cancelled) return
+        setGraphGovernance({
+          visibility: g?.visibility || 'private',
+          clubId: g?.club_id ?? null,
+        })
         const art = g?.planning_roadmap
         if (!art) return
         if (art.goal_query) setGoalQuery(String(art.goal_query))
@@ -1056,7 +1061,7 @@ export default function ExerciseProgressionPathBuilder({
     setQuickSaving(true)
     setQuickAiError('')
     try {
-      const payload = buildQuickCreateExercisePayloadFromDraft(quickCreateDraft)
+      const payload = buildQuickCreateExercisePayloadFromDraft(quickCreateDraft, graphGovernance)
       const created = await api.createExercise(payload)
       if (!created?.id) throw new Error('Anlegen fehlgeschlagen')
       insertExerciseFromOffer(created, activeOffer)

frontend/src/components/ProgressionFindingsPanel.jsx

@@ -11,6 +11,8 @@ import {
   formatRefineLogEntry,
   hasRematchSlotHints,
   pathQaQualityPercent,
+  pathQaHasSplitDimensions,
+  pathQaSubsectionPercent,
   pathQaShowsStrongResult,
   resolveHintSlotIndex,
   resolveOfferSlotIndex,
@@ -26,6 +28,46 @@ function severityStyle(pathQa) {
   }
 }
 
+function subsectionSeverityStyle(subsection) {
+  if (!subsection) return {}
+  return {
+    background: subsection.overall_ok
+      ? 'color-mix(in srgb, var(--accent) 6%, var(--surface))'
+      : 'color-mix(in srgb, var(--danger) 10%, var(--surface))',
+    border: `1px solid ${subsection.overall_ok ? 'var(--border)' : 'color-mix(in srgb, var(--danger) 35%, var(--border))'}`,
+  }
+}
+
+function PathQaDimensionBlock({ title, subsection, children = null }) {
+  if (!subsection) return null
+  const pct = pathQaSubsectionPercent(subsection)
+  return (
+    <div
+      style={{
+        marginTop: '10px',
+        padding: '8px 10px',
+        borderRadius: '8px',
+        fontSize: '11px',
+        lineHeight: 1.45,
+        ...subsectionSeverityStyle(subsection),
+      }}
+    >
+      <strong>
+        {title}: {subsection.overall_ok ? 'OK' : 'Hinweise'}
+        {pct != null ? ` (${pct} %)` : ''}
+      </strong>
+      {Array.isArray(subsection.issues) && subsection.issues.length > 0 ? (
+        <ul style={{ margin: '6px 0 0', paddingLeft: '16px', color: 'var(--text2)' }}>
+          {subsection.issues.slice(0, 5).map((issue) => (
+            <li key={`${title}-${issue}`}>{issue}</li>
+          ))}
+        </ul>
+      ) : null}
+      {children}
+    </div>
+  )
+}
+
 function PathQaPipelineDetails({ pathQa, fairQa = null, draft, title, compact = false }) {
   const { fixHints: optimizationHints } = useMemo(
     () => splitPathQaHints(pathQa),
@@ -310,6 +352,9 @@ export default function ProgressionFindingsPanel({
     && (canOptimizeCompare || pathQa?.assignments_preserved || showRematchAction)
   const qualityPct = pathQaQualityPercent(pathQa)
   const strongResult = pathQaShowsStrongResult(pathQa)
+  const hasSplitQa = pathQaHasSplitDimensions(pathQa)
+  const roadmapQa = pathQa?.roadmap_qa || null
+  const assignmentQa = pathQa?.assignment_qa || null
 
   return (
     <div className="card" style={{ position: 'sticky', top: '12px' }}>
@@ -364,9 +409,14 @@ export default function ProgressionFindingsPanel({
           }}
         >
           <strong>
-            Pfad-QS: {pathQa.overall_ok ? 'OK' : 'Hinweise'}
+            Pfad-QS gesamt: {pathQa.overall_ok ? 'OK' : 'Hinweise'}
             {qualityPct != null ? ` (${qualityPct} %)` : ''}
           </strong>
+          {hasSplitQa ? (
+            <p style={{ margin: '6px 0 0', color: 'var(--text2)', fontSize: '11px' }}>
+              Gesamt = schwächere Dimension (Roadmap vs. Übungsbesetzung).
+            </p>
+          ) : null}
           {pathQa.assignments_preserved ? (
             <p style={{ margin: '6px 0 0', color: 'var(--text2)', fontSize: '11px' }}>
               Bewertung des aktuellen Pfads. „Übungen matchen“ öffnet einen Dialog mit Vorschlägen für
@@ -378,7 +428,23 @@ export default function ProgressionFindingsPanel({
               Starker Pfad — KI-Empfehlungen unten können Feinschliff oder optionale Vertiefung sein.
             </p>
           ) : null}
-          {pathQa.topic_coverage ? (
+          {hasSplitQa ? (
+            <>
+              <PathQaDimensionBlock title="Roadmap & Stufen" subsection={roadmapQa}>
+                {roadmapQa?.topic_coverage ? (
+                  <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{roadmapQa.topic_coverage}</p>
+                ) : null}
+              </PathQaDimensionBlock>
+              <PathQaDimensionBlock title="Übungsbesetzung" subsection={assignmentQa}>
+                {assignmentQa?.empty_slot_count > 0 ? (
+                  <p style={{ margin: '6px 0 0', color: 'var(--danger)' }}>
+                    {assignmentQa.empty_slot_count} leere Slot(s) — „Übungen matchen“ oder manuell befüllen.
+                  </p>
+                ) : null}
+              </PathQaDimensionBlock>
+            </>
+          ) : null}
+          {!hasSplitQa && pathQa.topic_coverage ? (
             <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{pathQa.topic_coverage}</p>
           ) : null}
           {highlightTexts.length > 0 ? (
@@ -415,7 +481,7 @@ export default function ProgressionFindingsPanel({
               </ul>
             </>
           ) : null}
-          {Array.isArray(pathQa.issues) && pathQa.issues.length > 0 ? (
+          {Array.isArray(pathQa.issues) && pathQa.issues.length > 0 && !hasSplitQa ? (
             <ul style={{ margin: '6px 0 0', paddingLeft: '16px', color: 'var(--text2)' }}>
               {pathQa.issues.map((issue) => (
                 <li key={issue}>{issue}</li>

frontend/src/components/ProgressionGraphEditor.jsx

@@ -4,6 +4,8 @@
 import React, { useCallback, useEffect, useMemo, useState } from 'react'
 import { Link } from 'react-router-dom'
 import api from '../utils/api'
+import { useAuth } from '../context/AuthContext'
+import { getDefaultClubIdForGovernanceForms } from '../utils/activeClub'
 import ExercisePickerModal from './ExercisePickerModal'
 import ExerciseGapFillPrepModal from './exercises/ExerciseGapFillPrepModal'
 import ProgressionSlotCard from './ProgressionSlotCard'
@@ -37,7 +39,6 @@ import {
   compareDiffsForDialog,
   dedupeGapOffersBySlot,
   draftHasLibrarySlotAssignments,
-  draftRetrievalBoostExerciseIds,
   EMPTY_PLANNING_CATALOG_CONTEXT,
   filterGapOffersForUnfilledSlots,
   hydrateProgressionGraphDraft,
@@ -85,6 +86,7 @@ function resolveDefaultFocusAreaId(targetSummary, focusAreas) {
 }
 
 export default function ProgressionGraphEditor({ graphId, embedded = false, onSaved }) {
+  const { user } = useAuth()
   const [graphMeta, setGraphMeta] = useState(null)
   const [draft, setDraft] = useState(null)
   const [busy, setBusy] = useState(false)
@@ -475,28 +477,6 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     }
   }
 
-  const buildMatchRequestBase = (synced) => {
-    const override = majorStepsToOverridePayload(synced.slots)
-    return {
-      query: (synced.goalQuery || '').trim(),
-      max_steps: synced.slots.length,
-      include_llm_intent: true,
-      include_path_qa: true,
-      include_llm_path_qa: true,
-      include_path_reorder: false,
-      include_ai_gap_fill: true,
-      include_roadmap_preview: true,
-      include_llm_roadmap: false,
-      roadmap_first: true,
-      roadmap_override: override,
-      slot_assignments: slotsToSlotAssignments(synced),
-      retrieval_boost_exercise_ids: draftRetrievalBoostExerciseIds(synced),
-      progression_graph_id: Number(graphId),
-      ...roadmapStructuredPayload(synced.startSituation, synced.targetState, synced.roadmapNotes),
-      ...catalogApiPayload,
-    }
-  }
-
   const runMatchCompareFlow = async (synced, { source = 'match' } = {}) => {
     setMatchNotice('Schritt 1/2: Pfad bewerten (wie „Graph bewerten“)…')
     const baselineRes = await fetchPathEvaluate(synced)
@@ -880,7 +860,17 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     setSlotQuickSaving(true)
     setSlotQuickError('')
     try {
-      const payload = buildQuickCreateExercisePayloadFromDraft(slotQuickCreateDraft)
+      const graphVis = (graphMeta?.visibility || 'private').trim().toLowerCase()
+      const graphClubId =
+        graphMeta?.club_id != null
+          ? graphMeta.club_id
+          : graphVis === 'club'
+            ? getDefaultClubIdForGovernanceForms(user)
+            : null
+      const payload = buildQuickCreateExercisePayloadFromDraft(slotQuickCreateDraft, {
+        visibility: graphVis,
+        clubId: graphClubId,
+      })
       const created = await api.createExercise(payload)
       if (!created?.id) throw new Error('Anlegen fehlgeschlagen')
       setDraft((prev) => ({
@@ -1149,7 +1139,7 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
 
           {draft.slots.map((slot, idx) => (
             <ProgressionSlotCard
-              key={`slot-${idx}-${slot.learning_goal?.slice(0, 12) || 'x'}`}
+              key={`slot-${idx}`}
               slot={slot}
               slotIndex={idx}
               slotCount={draft.slots.length}

frontend/src/components/ProgressionOptimizeCompareModal.jsx

@@ -6,7 +6,9 @@ import FormModalOverlay from './FormModalOverlay'
 import {
   compareSlotReviews,
   defaultSelectedCompareDiffs,
+  pathQaHasSplitDimensions,
   pathQaQualityPercent,
+  pathQaSubsectionPercent,
   qualityDeltaPercent,
   rejectedCompareDiffs,
   slotFitScorePercent,
@@ -189,6 +191,9 @@ function SlotReviewRow({ review, selected, onToggle, applying }) {
             <strong>KI-Vorschlag nutzen</strong>
             <span style={{ display: 'block', fontSize: '11px', color: 'var(--text3)', marginTop: '2px' }}>
               {ai.title_hint || 'Neue Übung per KI entwerfen'}
+              {ai.auto_select
+                ? ' — empfohlen, Bibliothek passt nicht ausreichend zum Stufen-Ziel'
+                : ''}
             </span>
           </span>
         </label>
@@ -223,6 +228,9 @@ export default function ProgressionOptimizeCompareModal({
 
   const baselineQa = comparison.baseline_path_qa
   const baselinePct = pathQaQualityPercent(baselineQa)
+  const hasSplitQa = pathQaHasSplitDimensions(baselineQa)
+  const roadmapPct = pathQaSubsectionPercent(baselineQa?.roadmap_qa)
+  const assignmentPct = pathQaSubsectionPercent(baselineQa?.assignment_qa)
   const rejectedCount = rejected.length
   const reviewError = comparison.review_error || null
 
@@ -258,8 +266,9 @@ export default function ProgressionOptimizeCompareModal({
           {title}
         </h3>
         <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
-          Je Slot: aktuelle Bewertung, beste Bibliotheks-Alternative und optional KI. Haken nur
-          vorausgewählt, wenn die Alternative einen höheren Stufen-Fit hat.
+          Je Slot: aktuelle Bewertung, beste Bibliotheks-Alternative und optional KI. Vorauswahl:
+          Bibliothek nur bei klar besserem Stufen-Fit; bei leeren oder schwach passenden Slots eher
+          KI-Vorschlag.
         </p>
 
         {reviewError ? (
@@ -289,9 +298,19 @@ export default function ProgressionOptimizeCompareModal({
         >
           <strong>Dein Pfad</strong>
           <div style={{ marginTop: '6px' }}>{qaLabel(baselineQa)}</div>
-          {baselineQa?.topic_coverage ? (
+          {hasSplitQa ? (
+            <div style={{ marginTop: '6px', fontSize: '11px', color: 'var(--text2)', lineHeight: 1.45 }}>
+              Roadmap {roadmapPct != null ? `${roadmapPct} %` : '—'}
+              {' · '}
+              Besetzung {assignmentPct != null ? `${assignmentPct} %` : '—'}
+            </div>
+          ) : null}
+          {!hasSplitQa && baselineQa?.topic_coverage ? (
             <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{baselineQa.topic_coverage}</p>
           ) : null}
+          {hasSplitQa && baselineQa?.roadmap_qa?.topic_coverage ? (
+            <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{baselineQa.roadmap_qa.topic_coverage}</p>
+          ) : null}
         </div>
 
         {rejectedCount > 0 ? (

frontend/src/components/admin/CatalogPromptSlotsEditor.jsx

@@ -0,0 +1,166 @@
+import { useCallback, useEffect, useMemo, useState } from 'react'
+import { api } from '../../utils/api'
+
+const KIND_LABELS = {
+  focus_area: 'Fokusbereich',
+  training_type: 'Trainingsstil',
+  target_group: 'Zielgruppe',
+  style_direction: 'Stilrichtung',
+}
+
+/**
+ * Pflege der Katalog-Prompt-Slots (Planungs-KI) an einem Stammdaten-Eintrag.
+ */
+export default function CatalogPromptSlotsEditor({ catalogKind, catalogId, entryName = '' }) {
+  const [slotTypes, setSlotTypes] = useState([])
+  const [slots, setSlots] = useState({})
+  const [loading, setLoading] = useState(false)
+  const [saving, setSaving] = useState(false)
+  const [error, setError] = useState('')
+  const [loaded, setLoaded] = useState(false)
+  const [storedSlots, setStoredSlots] = useState({})
+
+  const applicableTypes = useMemo(() => {
+    const kind = (catalogKind || '').trim()
+    return (slotTypes || []).filter((t) => {
+      const kinds = t.applicable_kinds || []
+      return kinds.length === 0 || kinds.includes(kind)
+    })
+  }, [slotTypes, catalogKind])
+
+  const load = useCallback(async () => {
+    if (!catalogId || !catalogKind) return
+    setLoading(true)
+    setError('')
+    try {
+      const [typesRes, slotsRes] = await Promise.all([
+        api.listCatalogPromptSlotTypes(),
+        api.getCatalogPromptSlots(catalogKind, catalogId),
+      ])
+      setSlotTypes(Array.isArray(typesRes?.slot_types) ? typesRes.slot_types : [])
+      setSlots(slotsRes?.slots && typeof slotsRes.slots === 'object' ? { ...slotsRes.slots } : {})
+      setStoredSlots(
+        slotsRes?.stored_slots && typeof slotsRes.stored_slots === 'object' ? { ...slotsRes.stored_slots } : {}
+      )
+      setLoaded(true)
+    } catch (e) {
+      setError(e.message || String(e))
+    } finally {
+      setLoading(false)
+    }
+  }, [catalogKind, catalogId])
+
+  useEffect(() => {
+    setLoaded(false)
+    setSlots({})
+    if (catalogId && catalogKind) {
+      load()
+    }
+  }, [catalogId, catalogKind, load])
+
+  async function handleSave() {
+    if (!catalogId || !catalogKind) return
+    setSaving(true)
+    setError('')
+    try {
+      const res = await api.updateCatalogPromptSlots(catalogKind, catalogId, { slots })
+      setSlots(res?.slots && typeof res.slots === 'object' ? { ...res.slots } : {})
+    } catch (e) {
+      setError(e.message || String(e))
+    } finally {
+      setSaving(false)
+    }
+  }
+
+  if (!catalogId || !catalogKind) {
+    return null
+  }
+
+  const kindLabel = KIND_LABELS[catalogKind] || catalogKind
+
+  return (
+    <div
+      className="catalog-prompt-slots"
+      style={{
+        marginTop: '20px',
+        paddingTop: '16px',
+        borderTop: '1px solid var(--border)',
+      }}
+    >
+      <h4 style={{ margin: '0 0 8px' }}>Planungs-KI — Prompt-Texte</h4>
+      <p style={{ margin: '0 0 12px', fontSize: '13px', color: 'var(--text2)' }}>
+        Texte für KI-Prompts (Progressionsgraph, Pfad-QS). Platzhalter:{' '}
+        <code>{'{{' + catalogKind + '_<slot_key>}}'}</code>
+        {entryName ? (
+          <>
+            {' '}
+            — Eintrag: <strong>{entryName}</strong>
+          </>
+        ) : null}
+      </p>
+
+      {error ? (
+        <div className="admin-matrix-alert" style={{ marginBottom: '12px' }}>
+          {error}
+        </div>
+      ) : null}
+
+      {loading && !loaded ? (
+        <div className="spinner" style={{ minHeight: '48px' }} />
+      ) : (
+        <>
+          {applicableTypes.map((st) => {
+            const key = st.slot_key
+            const ph = `{{${catalogKind}_${key}}}`
+            const isCodeOnly = st.for_code && !st.for_llm
+            const fromFallback =
+              !(storedSlots[key] || '').trim() && (slots[key] || '').trim() && key !== 'description'
+            return (
+              <div key={key} className="form-row">
+                <label className="form-label">
+                  {st.display_name || key}
+                  {fromFallback ? (
+                    <span style={{ marginLeft: '6px', fontSize: '12px', color: 'var(--accent)' }}>
+                      (Standard-Vorlage)
+                    </span>
+                  ) : null}
+                  {isCodeOnly ? (
+                    <span style={{ marginLeft: '6px', fontSize: '12px', color: 'var(--text3)' }}>
+                      (primär Code)
+                    </span>
+                  ) : null}
+                </label>
+                {st.description ? (
+                  <p style={{ margin: '0 0 6px', fontSize: '12px', color: 'var(--text3)' }}>{st.description}</p>
+                ) : null}
+                <textarea
+                  className="form-input"
+                  rows={key === 'description' ? 3 : 4}
+                  value={slots[key] || ''}
+                  onChange={(e) => setSlots((prev) => ({ ...prev, [key]: e.target.value }))}
+                  placeholder={
+                    key === 'description'
+                      ? 'Leer = Stammdaten-Beschreibung als Fallback'
+                      : `Text für ${ph} …`
+                  }
+                />
+                <p style={{ margin: '4px 0 0', fontSize: '11px', color: 'var(--text3)', fontFamily: 'monospace' }}>
+                  {ph}
+                </p>
+              </div>
+            )
+          })}
+
+          <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap', marginTop: '8px' }}>
+            <button type="button" className="btn btn-primary" onClick={handleSave} disabled={saving || loading}>
+              {saving ? 'Speichert…' : 'KI-Texte speichern'}
+            </button>
+            <button type="button" className="btn btn-secondary" onClick={load} disabled={loading || saving}>
+              Neu laden
+            </button>
+          </div>
+        </>
+      )}
+    </div>
+  )
+}

frontend/src/components/admin/DetailPanel.jsx

@@ -1,5 +1,6 @@
 import React, { useState } from 'react'
 import { api } from '../../utils/api'
+import CatalogPromptSlotsEditor from './CatalogPromptSlotsEditor'
 
 function DetailPanel({ item, onUpdate, focusAreas }) {
   const type = item._type
@@ -87,6 +88,7 @@ function FocusAreaDetail({ item, onUpdate }) {
         </button>
         <button type="button" className="btn btn-danger" onClick={handleDelete}>Löschen</button>
       </div>
+      <CatalogPromptSlotsEditor catalogKind="focus_area" catalogId={item.id} entryName={form.name} />
     </div>
   )
 }
@@ -169,6 +171,7 @@ function StyleDirectionDetail({ item, onUpdate, focusAreas }) {
         </button>
         <button type="button" className="btn btn-danger" onClick={handleDelete}>Löschen</button>
       </div>
+      <CatalogPromptSlotsEditor catalogKind="style_direction" catalogId={item.id} entryName={form.name} />
     </div>
   )
 }
@@ -251,6 +254,7 @@ function TrainingTypeDetail({ item, onUpdate, focusAreas }) {
         </button>
         <button type="button" className="btn btn-danger" onClick={handleDelete}>Löschen</button>
       </div>
+      <CatalogPromptSlotsEditor catalogKind="training_type" catalogId={item.id} entryName={form.name} />
     </div>
   )
 }

frontend/src/pages/AdminAiPromptsPage.jsx

@@ -31,8 +31,22 @@ export default function AdminAiPromptsPage() {
   const [pvExec, setPvExec] = useState('<p>Ablauf hier</p>')
   const [pvHint, setPvHint] = useState('')
   const [pvFocusId, setPvFocusId] = useState('')
+  const [pvGoalQuery, setPvGoalQuery] = useState(
+    'Mae Geri vom Grundschritt bis zur kontrollierten Kumite-Nähe'
+  )
+  const [pvUserNotes, setPvUserNotes] = useState('Fokus Breitensport, ohne Wettkampfdruck.')
+  const [pvMaxSteps, setPvMaxSteps] = useState('5')
+  const [pvSearchQuery, setPvSearchQuery] = useState('')
   const [pvPreview, setPvPreview] = useState(null)
 
+  const selectedSlug = (detail?.slug || '').trim().toLowerCase()
+  const isExercisePreviewSlug = [
+    'exercise_summary',
+    'exercise_skill_suggestions',
+    'exercise_instruction_rewrite',
+  ].includes(selectedSlug)
+  const isPlanningPreviewSlug = selectedSlug.startsWith('planning_')
+
   const loadList = useCallback(async () => {
     const [pList, cat] = await Promise.all([
       api.listAdminAiPrompts(),
@@ -133,16 +147,24 @@ export default function AdminAiPromptsPage() {
     if (!detail?.id) return
     setError('')
     try {
-      const body = {
-        title: pvTitle,
-        goal: pvGoal,
-        execution: pvExec,
-        focus_hint: pvHint || undefined,
-      }
+      const body = {}
+      if (isPlanningPreviewSlug) {
+        body.goal_query = pvGoalQuery.trim() || undefined
+        body.user_notes = pvUserNotes.trim() || undefined
+        const ms = parseInt(String(pvMaxSteps).trim(), 10)
+        if (Number.isFinite(ms) && ms >= 2 && ms <= 10) body.max_steps = ms
+        const sq = pvSearchQuery.trim()
+        if (sq) body.search_query = sq
+      } else if (isExercisePreviewSlug) {
+        body.title = pvTitle
+        body.goal = pvGoal
+        body.execution = pvExec
+        body.focus_hint = pvHint || undefined
         const fid = parseInt(String(pvFocusId).trim(), 10)
         if (Number.isFinite(fid) && fid >= 1) {
           body.focus_areas_context = [{ focus_area_id: fid, is_primary: true }]
         }
+      }
       const r = await api.previewAdminAiPrompt(detail.id, body)
       setPvPreview(r)
     } catch (e) {
@@ -171,8 +193,8 @@ export default function AdminAiPromptsPage() {
         <h1 style={{ margin: 0, fontSize: '1.25rem' }}>KI Prompts</h1>
       </div>
       <p style={{ fontSize: '13px', color: 'var(--text3)', marginTop: 0 }}>
-        Datenbankvorlagen (<code>ai_prompts</code>) für Übungs-KI. Platzhalter im Mustache-Stil werden serverseitig
-        aufgelöst — die Vorschau unten ruft kein externes Modell auf.
+        Datenbankvorlagen (<code>ai_prompts</code>) für Übungs- und Planungs-KI. Platzhalter im Mustache-Stil werden
+        serverseitig aufgelöst — die Vorschau unten ruft kein externes Modell auf.
       </p>
       {error ? <p style={{ color: 'var(--danger)' }}>{error}</p> : null}
 
@@ -301,6 +323,55 @@ export default function AdminAiPromptsPage() {
 
               <section style={{ marginTop: 20, paddingTop: 16, borderTop: '1px solid var(--border)' }}>
                 <h4 style={{ margin: '0 0 12px', fontSize: '15px' }}>Vorschau (ohne OpenRouter)</h4>
+                {isPlanningPreviewSlug ? (
+                  <>
+                    <p style={{ fontSize: 13, color: 'var(--text3)', marginTop: 0 }}>
+                      Beispielkontext für Planungs-Prompts — echte Katalog-Auszüge aus der Datenbank, übrige Felder
+                      sind repräsentative Demo-Daten.
+                    </p>
+                    <div className="form-row">
+                      <label className="form-label">Zielanfrage (goal_query)</label>
+                      <textarea
+                        className="form-input"
+                        rows={3}
+                        value={pvGoalQuery}
+                        onChange={(e) => setPvGoalQuery(e.target.value)}
+                      />
+                    </div>
+                    <div className="form-row">
+                      <label className="form-label">Trainer-Notizen (user_notes)</label>
+                      <textarea
+                        className="form-input"
+                        rows={2}
+                        value={pvUserNotes}
+                        onChange={(e) => setPvUserNotes(e.target.value)}
+                      />
+                    </div>
+                    <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 12 }}>
+                      <div className="form-row">
+                        <label className="form-label">max_steps (Roadmap)</label>
+                        <input
+                          className="form-input"
+                          type="number"
+                          min={2}
+                          max={10}
+                          value={pvMaxSteps}
+                          onChange={(e) => setPvMaxSteps(e.target.value)}
+                        />
+                      </div>
+                      <div className="form-row">
+                        <label className="form-label">Suchanfrage (optional)</label>
+                        <input
+                          className="form-input"
+                          placeholder="Leer = goal_query"
+                          value={pvSearchQuery}
+                          onChange={(e) => setPvSearchQuery(e.target.value)}
+                        />
+                      </div>
+                    </div>
+                  </>
+                ) : isExercisePreviewSlug ? (
+                  <>
                     <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 12 }}>
                       <div className="form-row">
                         <label className="form-label">Titel</label>
@@ -328,6 +399,13 @@ export default function AdminAiPromptsPage() {
                       <label className="form-label">Durchführung (HTML möglich)</label>
                       <textarea className="form-input" rows={4} value={pvExec} onChange={(e) => setPvExec(e.target.value)} />
                     </div>
+                  </>
+                ) : (
+                  <p style={{ fontSize: 13, color: 'var(--text3)' }}>
+                    Für diesen Slug ist noch kein Beispielkontext hinterlegt — es wird nur das Roh-Template ohne
+                    Ersetzung angezeigt.
+                  </p>
+                )}
                 <button type="button" className="btn btn-secondary" onClick={() => runPreview()}>
                   Platzhalter auflösen
                 </button>

frontend/src/pages/AdminCatalogsPage.jsx

@@ -2,6 +2,7 @@ import { useState, useEffect } from 'react'
 import { api } from '../utils/api'
 import AdminPageNav from '../components/AdminPageNav'
 import PageSectionNav from '../components/PageSectionNav'
+import CatalogPromptSlotsEditor from '../components/admin/CatalogPromptSlotsEditor'
 
 const CATALOG_SUBTABS = [
   { id: 'focus-areas', label: 'Fokusbereiche' },
@@ -62,6 +63,38 @@ export default function AdminCatalogsPage() {
   // M:N Assignment Matrix
   const [assignments, setAssignments] = useState([])
   const [matrixLoading, setMatrixLoading] = useState(false)
+  const [openKiSlots, setOpenKiSlots] = useState(null)
+
+  function toggleKiSlots(kind, id) {
+    const key = `${kind}:${id}`
+    setOpenKiSlots((prev) => (prev === key ? null : key))
+  }
+
+  function renderKiSlotsToggle(kind, id, label = 'KI-Planungstexte') {
+    const key = `${kind}:${id}`
+    const open = openKiSlots === key
+    return (
+      <button
+        type="button"
+        className="btn btn-secondary"
+        onClick={() => toggleKiSlots(kind, id)}
+      >
+        {open ? 'KI-Texte ausblenden' : label}
+      </button>
+    )
+  }
+
+  function renderKiSlotsPanel(kind, id, entryName) {
+    const key = `${kind}:${id}`
+    if (openKiSlots !== key) return null
+    return (
+      <CatalogPromptSlotsEditor
+        catalogKind={kind}
+        catalogId={id}
+        entryName={entryName}
+      />
+    )
+  }
 
   useEffect(() => {
     loadData()
@@ -75,14 +108,22 @@ export default function AdminCatalogsPage() {
         const data = await api.listFocusAreas()
         setFocusAreas(data)
       } else if (activeTab === 'training-styles') {
-        const data = await api.listStyleDirections()
+        const [data, areas] = await Promise.all([
+          api.listStyleDirections(),
+          api.listFocusAreas(),
+        ])
         setTrainingStyles(data)
+        setFocusAreas(areas)
       } else if (activeTab === 'training-characters') {
         const data = await api.listTrainingCharacters()
         setTrainingCharacters(data)
       } else if (activeTab === 'training-types') {
-        const data = await api.listTrainingTypes()
+        const [data, areas] = await Promise.all([
+          api.listTrainingTypes(),
+          api.listFocusAreas(),
+        ])
         setTrainingTypes(data)
+        setFocusAreas(areas)
       } else if (activeTab === 'skill-categories') {
         const data = await api.listSkillCategories()
         setSkillCategories(data)
@@ -431,6 +472,11 @@ export default function AdminCatalogsPage() {
                           <button className="btn btn-primary" onClick={() => updateFocusArea(fa.id, editingFA)}>Speichern</button>
                           <button className="btn" onClick={() => setEditingFA(null)}>Abbrechen</button>
                         </div>
+                        <CatalogPromptSlotsEditor
+                          catalogKind="focus_area"
+                          catalogId={fa.id}
+                          entryName={editingFA.name}
+                        />
                       </div>
                     ) : (
                       <div>
@@ -449,10 +495,12 @@ export default function AdminCatalogsPage() {
                             }}
                           />
                         </div>
-                        <div style={{ display: 'flex', gap: '8px' }}>
+                        <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap' }}>
                           <button className="btn" onClick={() => setEditingFA(fa)}>Bearbeiten</button>
+                          {renderKiSlotsToggle('focus_area', fa.id)}
                           <button className="btn" onClick={() => deleteFocusArea(fa.id)}>Löschen</button>
                         </div>
+                        {renderKiSlotsPanel('focus_area', fa.id, fa.name)}
                       </div>
                     )}
                   </div>
@@ -539,6 +587,11 @@ export default function AdminCatalogsPage() {
                           <button className="btn btn-primary" onClick={() => updateStyleDirection(ts.id, editingTS)}>Speichern</button>
                           <button className="btn" onClick={() => setEditingTS(null)}>Abbrechen</button>
                         </div>
+                        <CatalogPromptSlotsEditor
+                          catalogKind="style_direction"
+                          catalogId={ts.id}
+                          entryName={editingTS.name}
+                        />
                       </div>
                     ) : (
                       <div>
@@ -554,10 +607,12 @@ export default function AdminCatalogsPage() {
                           </p>
                         )}
                         <p style={{ margin: '8px 0', color: 'var(--text2)' }}>{ts.description}</p>
-                        <div style={{ display: 'flex', gap: '8px' }}>
+                        <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap' }}>
                           <button className="btn" onClick={() => setEditingTS(ts)}>Bearbeiten</button>
+                          {renderKiSlotsToggle('style_direction', ts.id)}
                           <button className="btn" onClick={() => deleteStyleDirection(ts.id)}>Löschen</button>
                         </div>
+                        {renderKiSlotsPanel('style_direction', ts.id, ts.name)}
                       </div>
                     )}
                   </div>
@@ -730,6 +785,11 @@ export default function AdminCatalogsPage() {
                           <button className="btn btn-primary" onClick={() => updateTrainingType(tt.id, editingTT)}>Speichern</button>
                           <button className="btn" onClick={() => setEditingTT(null)}>Abbrechen</button>
                         </div>
+                        <CatalogPromptSlotsEditor
+                          catalogKind="training_type"
+                          catalogId={tt.id}
+                          entryName={editingTT.name}
+                        />
                       </div>
                     ) : (
                       <div>
@@ -744,10 +804,12 @@ export default function AdminCatalogsPage() {
                             <span style={{ color: 'var(--accent)' }}>{tt.focus_area_icon} {tt.focus_area_name}</span>
                           </p>
                         )}
-                        <div style={{ display: 'flex', gap: '8px', marginTop: '12px' }}>
+                        <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap', marginTop: '12px' }}>
                           <button className="btn" onClick={() => setEditingTT(tt)}>Bearbeiten</button>
+                          {renderKiSlotsToggle('training_type', tt.id)}
                           <button className="btn" onClick={() => deleteTrainingType(tt.id)}>Löschen</button>
                         </div>
+                        {renderKiSlotsPanel('training_type', tt.id, tt.name)}
                       </div>
                     )}
                   </div>
@@ -956,6 +1018,11 @@ export default function AdminCatalogsPage() {
                           <button className="btn btn-primary" onClick={() => updateTargetGroup(tg.id, tg)}>Speichern</button>
                           <button className="btn" onClick={() => setEditingTG(null)}>Abbrechen</button>
                         </div>
+                        <CatalogPromptSlotsEditor
+                          catalogKind="target_group"
+                          catalogId={tg.id}
+                          entryName={tg.name}
+                        />
                       </div>
                     ) : (
                       <div style={{ display: 'flex', alignItems: 'center', justifyContent: 'space-between' }}>
@@ -970,10 +1037,12 @@ export default function AdminCatalogsPage() {
                             <p style={{ margin: '8px 0 0 0', fontSize: '14px' }}>{tg.description}</p>
                           )}
                         </div>
-                        <div style={{ display: 'flex', gap: '8px' }}>
+                        <div style={{ display: 'flex', gap: '8px', flexWrap: 'wrap' }}>
                           <button className="btn" onClick={() => setEditingTG(tg.id)}>Bearbeiten</button>
+                          {renderKiSlotsToggle('target_group', tg.id)}
                           <button className="btn" style={{ background: 'var(--danger)', color: 'white' }} onClick={() => deleteTargetGroup(tg.id)}>Löschen</button>
                         </div>
+                        {renderKiSlotsPanel('target_group', tg.id, tg.name)}
                       </div>
                     )}
                   </div>

frontend/src/utils/api.js

@@ -626,6 +626,21 @@ export async function getAdminAiPromptPlaceholdersCatalog() {
   return request('/api/admin/ai-prompts/catalog/placeholders')
 }
 
+export async function listCatalogPromptSlotTypes() {
+  return request('/api/catalog-prompt-slot-types')
+}
+
+export async function getCatalogPromptSlots(catalogKind, catalogId) {
+  return request(`/api/catalog-prompt-slots/${encodeURIComponent(catalogKind)}/${catalogId}`)
+}
+
+export async function updateCatalogPromptSlots(catalogKind, catalogId, data) {
+  return request(`/api/catalog-prompt-slots/${encodeURIComponent(catalogKind)}/${catalogId}`, {
+    method: 'PUT',
+    body: JSON.stringify(data || {}),
+  })
+}
+
 // ============================================================================
 // Reifegradmodelle / Fähigkeitsmatrix
 // ============================================================================
@@ -1089,6 +1104,9 @@ export const api = {
   previewAdminAiPrompt,
   resetAdminAiPromptTemplate,
   getAdminAiPromptPlaceholdersCatalog,
+  listCatalogPromptSlotTypes,
+  getCatalogPromptSlots,
+  updateCatalogPromptSlots,
   listStyleDirections,
   listTrainingStyles,
   createStyleDirection,

frontend/src/utils/exerciseAiQuickCreate.js

@@ -208,11 +208,27 @@ export function aiPreviewToQuickCreateDraft(preview, { title, focusAreaId, sketc
   }
 }
 
+/**
+ * Sichtbarkeit/club_id für Schnellanlage (z. B. aus Progressionsgraph).
+ * @param {{ visibility?: string, clubId?: number|null }} [governance]
+ */
+function resolveQuickCreateGovernance(governance) {
+  const rawVis = (governance?.visibility || 'private').trim().toLowerCase()
+  const vis = rawVis === 'club' || rawVis === 'official' ? rawVis : 'private'
+  let clubId = null
+  if (vis === 'club' && governance?.clubId != null && governance.clubId !== '') {
+    const n = Number(governance.clubId)
+    if (Number.isFinite(n) && n > 0) clubId = n
+  }
+  return { visibility: vis, club_id: clubId }
+}
+
 /**
  * createExercise-Payload aus bearbeitetem Entwurf.
+ * @param {{ visibility?: string, clubId?: number|null }} [governance]
  * @throws {Error}
  */
-export function buildQuickCreateExercisePayloadFromDraft(draft) {
+export function buildQuickCreateExercisePayloadFromDraft(draft, governance) {
   const title = (draft?.title || '').trim()
   if (title.length < 3) {
     throw new Error('Titel: mindestens 3 Zeichen.')
@@ -239,6 +255,7 @@ export function buildQuickCreateExercisePayloadFromDraft(draft) {
   if (summary && !stripHtmlToText(summary).trim()) summary = null
 
   const skills = (draft?.skillChoices || []).filter((c) => c.include).map((c) => c.after)
+  const { visibility, club_id: clubId } = resolveQuickCreateGovernance(governance)
 
   return {
     title,
@@ -247,7 +264,7 @@ export function buildQuickCreateExercisePayloadFromDraft(draft) {
     execution,
     preparation: prep,
     trainer_notes: trainerNotes,
-    visibility: 'private',
+    visibility,
     status: 'draft',
     equipment: [],
     focus_areas_multi: [{ focus_area_id: fid, is_primary: true }],
@@ -256,15 +273,16 @@ export function buildQuickCreateExercisePayloadFromDraft(draft) {
     target_groups_multi: [],
     age_groups: [],
     skills,
-    club_id: null,
+    club_id: clubId,
   }
 }
 
 /**
  * createExercise-Payload aus bestätigter Vorschau (Checkbox-Modus).
+ * @param {{ visibility?: string, clubId?: number|null }} [governance]
  * @throws {Error}
  */
-export function buildQuickCreateExercisePayloadFromPreview(preview, { title, focusAreaId, sketchPlain }) {
+export function buildQuickCreateExercisePayloadFromPreview(preview, { title, focusAreaId, sketchPlain, ...governance } = {}) {
   const sketchHtml = aiPlainTextToMinimalHtml(sketchPlain)
   const fieldMap = {}
   for (const c of preview?.instructionChoices || []) {
@@ -288,6 +306,7 @@ export function buildQuickCreateExercisePayloadFromPreview(preview, { title, foc
   }
 
   const skills = (preview?.skillChoices || []).filter((c) => c.include).map((c) => c.after)
+  const { visibility, club_id: clubId } = resolveQuickCreateGovernance(governance)
 
   const fid = Number(focusAreaId)
   if (!Number.isFinite(fid) || fid < 1) {
@@ -301,7 +320,7 @@ export function buildQuickCreateExercisePayloadFromPreview(preview, { title, foc
     execution,
     preparation: prep,
     trainer_notes: trainerNotes,
-    visibility: 'private',
+    visibility,
     status: 'draft',
     equipment: [],
     focus_areas_multi: [{ focus_area_id: fid, is_primary: true }],
@@ -310,7 +329,7 @@ export function buildQuickCreateExercisePayloadFromPreview(preview, { title, foc
     target_groups_multi: [],
     age_groups: [],
     skills,
-    club_id: null,
+    club_id: clubId,
   }
 }
 

frontend/src/utils/progressionGraphDraft.js

@@ -173,8 +173,19 @@ export function pathQaQualityPercent(pathQa) {
   return Math.round(Number(pathQa.quality_score) * 100)
 }
 
+export function pathQaSubsectionPercent(subsection) {
+  if (subsection?.quality_score == null || !Number.isFinite(Number(subsection.quality_score))) return null
+  return Math.round(Number(subsection.quality_score) * 100)
+}
+
+export function pathQaHasSplitDimensions(pathQa) {
+  return Boolean(pathQa?.roadmap_qa || pathQa?.assignment_qa)
+}
+
 export function pathQaShowsStrongResult(pathQa) {
   const pct = pathQaQualityPercent(pathQa)
+  const assignmentOk = pathQa?.assignment_qa ? pathQa.assignment_qa.overall_ok !== false : true
+  if (!assignmentOk) return false
   if (pathQa?.overall_ok && pct != null && pct >= 85) return true
   return Boolean(pathQa?.overall_ok && pct != null && pct >= 80 && !(pathQa?.issues || []).length)
 }
@@ -1073,9 +1084,16 @@ export function compareDiffsForDialog(comparison) {
 export function defaultSelectedCompareDiffs(comparison) {
   const reviews = compareSlotReviews(comparison)
   if (reviews.length > 0) {
-    return reviews
-      .filter((review) => review?.library_alternative?.auto_select)
-      .map((review) => slotReviewSelectionKey(review.roadmap_major_step_index, 'library'))
+    const keys = []
+    for (const review of reviews) {
+      const midx = review.roadmap_major_step_index
+      if (review?.ai_alternative?.auto_select) {
+        keys.push(slotReviewSelectionKey(midx, 'ai'))
+      } else if (review?.library_alternative?.auto_select) {
+        keys.push(slotReviewSelectionKey(midx, 'library'))
+      }
+    }
+    return keys
   }
   return compareDiffsForDialog(comparison).map((d) => Number(d.roadmap_major_step_index))
 }